Methods and compositions involving mir-135b for distinguishing pancreatic cancer from benign pancreatic disease

ABSTRACT

Provided are methods and compositions for identifying a miRNA profile for a particular condition, such as pancreatic disease, and using the profile in assessing the condition of a patient.

This application claims priority to U.S. Provisional Application Ser.No. 61/534,332, filed Sep. 13, 2011, and U.S. Provisional ApplicationSer. No. 61/536,486, filed Sep. 19, 2011, both of which are incorporatedby reference in their entirety.

The invention was made with government support under Grant No.P50CA062924 awarded by the National Institutes of Health and Grant No.R44CA118785 from the National Institutes of Health and the NationalCancer Institute. The government has certain rights in the invention.

BACKGROUND OF THE INVENTION

I. Field of the Invention

The invention relates generally to the field of molecular biology. Moreparticularly, it concerns methods and compositions involving microRNAmolecules (miRNAs). Certain aspects of the invention includeapplications for miRNAs in diagnostics, therapeutics, and prognosticsfor pancreatic cancer.

II. Background

In 2001, several groups used a cloning method to isolate and identify alarge group of “microRNAs” (miRNAs) from C. elegans, Drosophila, andhuman s (Lagos-Quintana et al., 2001; Lau et al., 2001; Lee and Ambros,2001). Several hundreds of miRNAs have been identified in plants andanimals—including humans—which do not appear to have endogenous siRNAs.Thus, while similar to siRNAs, miRNAs are nonetheless distinct.

miRNAs thus far observed have been approximately 21-22 nucleotides inlength and arise from longer precursors, which are transcribed fromnon-protein-encoding genes. See review of Carrington et al. (2003). Theprecursors form structures that fold back on themselves inself-complementary regions; they are then processed by the nucleaseDicer in animals or DCL1 in plants. miRNA molecules interrupttranslation through precise or imprecise base-pairing with theirtargets.

Many miRNAs are conserved among diverse organisms, and this has led tothe suggestion that miRNAs are involved in essential biologicalprocesses throughout the life span of an organism (Esquela-Kerscher andSlack, 2006). In particular, miRNAs have been implicated in regulatingcell growth and cell and tissue differentiation, cellular processes thatare associated with the development of cancer. For instance, lin-4 andlet-7 both regulate passage from one larval state to another during C.elegans development (Ambros, 2001). miR-14 and bantam are DrosophilamiRNAs that regulate cell death, apparently by regulating the expressionof genes involved in apoptosis (Brennecke et al., 2003, Xu et al.,2003).

Research on miRNAs is increasing as scientists are beginning toappreciate the broad role that these molecules play in the regulation ofeukaryotic gene expression. In particular, several recent studies haveshown that expression levels of numerous miRNAs are associated withvarious cancers (reviewed in Esquela-Kerscher and Slack, 2006). Reducedexpression of two miRNAs correlates strongly with chronic lymphocyticleukemia in human s, providing a possible link between miRNAs and cancer(Calin et al., 2002). Others have evaluated the expression patterns oflarge numbers of miRNAs in multiple human cancers and observeddifferential expression of almost all miRNAs across numerous cancertypes (Lu et al., 2005). Most such studies link miRNAs to cancer only byindirect evidence. In contrast, a single study has provided more directevidence that miRNAs may contribute directly to causing cancer. Byforcing the over-expression of six miRNAs in mice, He et al. (2005)demonstrated a significant increase in B cell lymphomas.

Pancreatic cancer is a particularly challenging disease to diagnose andtreat. Each year about 33,000 people in the United States are diagnosedwith adenocarcinoma of the pancreas, and about 32,000 people die eachyear from pancreatic cancer (Jemal et al., 2006). Pancreatic carcinomaranks as the fourth leading cause of cancer deaths in the United States,and the five year survival rate (−4%) is the lowest among all cancers(Jemal et al., 2006).

Most pancreatic cancers are adenocarcinomas of the ductal epithelium(Freelove and Walling, 2006)—or pancreatic ductal adenocarcinomas(PDAC). PDAC is characterized by its late clinical presentation, earlyand aggressive local invasion and high metastatic potential. The lack ofsensitive early detection strategies and its strong resistance tochemotherapy and radiation therapy compounds the overall very poorprognosis of PDAC, which has a median survival time following diagnosisof 3-5 months. Currently, effective diagnostic methods and/or treatmentsfor pancreatic cancer are lacking (Monti et al., 2004). Surgery is stillthe only effective treatment option, improving the median survival timeto 10-20 months; however, at the time of diagnosis only 20% of PDACs areamenable to surgery and cure is rarely achieved (See Yeo et al., 2002).Thus, improved early diagnosis modalities as well as new therapeutictargets for the development of effective treatment strategies areurgently needed to improve the dismal prognosis of PDAC.

Distinguishing between chronic pancreatitis and pancreatic cancer can beextremely difficult. Symptoms are frequently non-specific and limited tojaundice, weight loss and bruising. Many patients with chronicpancreatitis (non-cancerous condition) exhibit the same symptoms aspatients with PDAC. Serum levels of certain proteins may be suggestiveof pancreatic adenocarcinoma but are not diagnostic; and the serum tumormarker CA19-9 can help confirm pancreatic cancer diagnosis, but isineffective as a patient screening tool (Freelove and Walling, 2006). Aneed exists for additional diagnostic assays that can assess thecondition of the pancreas in general and distinguish a patient with PDACfrom a patient suffering from chronic pancreatitis or a patient with ahealthy pancreas.

SUMMARY OF THE INVENTION

The disclosed methods and compositions overcome these problems in theart by providing ways to use the expression of different miRNAs asbiomarkers to distinguish between abnormal pancreatic cells. Embodimentsconcern differentiating diseased, normal, cancerous, and/or abnormaltissues, including but not limited to normal pancreas, non-cancerousdiseased pancreas such as pancreatitis, and pancreatic cancer (e.g.,pancreatic ductal adenocarcinomas (PDAC)). Further, method are providedfor diagnosing diseased, normal, cancerous, and/or abnormal tissues,including but not limited to pancreatic cancer and chronic pancreatitisthat is based on determining expression levels of selected miRNAs inpatient-derived samples that contain pancreatic cells. Additionalmethods provide information for assessing whether a patient withabnormal or aberrant pancreatic cells has PDAC.

Disclosed herein are methods for evaluating pancreatic cells from apatient to determine whether cells are cancerous or non-cancerous,whether cells are PDAC cells or CP cells, or whether cells are PDACcells or normal cells or benign cells. This provides a clinician withinformation useful for diagnosis and/or treatment options. It may alsoconfirm an assessment based on the cytology of the patient's pancreascells or on the patient's medical history or on the patient's symptomsor on some other test.

Methods involve obtaining information about the levels of expression ofcertain microRNAs or miRNAs whose expression levels differ in differenttypes of pancreatic cysts. In some embodiments, differences in miRNAexpression between or among different types of pancreas cells depends onwhether the cells are PDAC cells or are not PDAC cells. Such differencesare highlighted when expression levels are first compared among two ormore miRNAs and those differential values are compared to or contrastedwith the differential values of PDAC cells or either pancreatitis cellsor normal pancreatic cells. Embodiments concern methods and compositionsthat can be used for evaluating pancreas cells or a pancreas sample,differentiating PDAC cells, distinguishing PDAC from chronicpancreatitis, identifying a patient with PDAC or a patient with chronicpancreatitis, identifying PDAC cells as a target for surgical resection,determining PDAC cells should not be surgically resected, categorizingabnormal pancreatic cells, diagnosing PDAC or diagnosing benign pancreascells or diagnosis pancreatitis, providing a prognosis to a patientregarding abnormal pancreatic cells or symptoms of pancreatitis and/orPDAC, evaluating treatment options for PDAC, or treating a patient withPDAC. These methods can be implemented involving steps and compositionsdescribed below in different embodiments.

In some embodiments, methods involve measuring from a pancreatic samplefrom the patient the level of expression of at least one, two, three,four, five, six or seven of the following miRNAs: miR-135b, miR-148a,miR-24, miR-196a, miR-130b, miR-375, and/or miR-96. In certainembodiments, the level of expression of 1, 2, 3, 4, 5, 6, or 7 of thefollowing miRNAs, which may or may not be a diff pair miRNA, may bemeasured: miR-135b, miR-148a, miR-24, miR-196a, miR-130b, miR-375,and/or miR-96. In certain embodiments, methods involve measuring from apancreas sample from the patient the level of expression of at least oneof the following diff pair miRNAs: miR-135b, miR-148a, miR-24, miR-196a,miR-130b, miR-375, and/or miR-96, wherein at least one of the miRNAs isa biomarker miRNA. The term “diff pair miRNA” refers to a miRNA that isone member of a pair of miRNAs where the expression level of one miRNAof the diff pair in a sample is compared to the expression level of theother miRNA of the diff pair in the same sample. The expression levelsof two diff pair miRNAs may be evaluated with respect to each other,i.e., compared, which includes but is not limited to subtracting,dividing, multiplying or adding values representing the expressionlevels of the two diff pair miRNAs. The term “biomarker miRNA” refers toa miRNA whose expression level is indicative of a particular disease orcondition. A biomarker miRNA may be a diff pair miRNA in certainembodiments. As part of a diff pair, the level of expression of abiomarker miRNA may highlight or emphasize differences in miRNAexpression between different populations, such as PDAC cells from CPcells or from benign or normal pancreas cells. In some embodiments, whenmiRNA expression is different in a particular population relative toanother population, differences between miRNA expression levels can beincreased, highlighted, emphasized, or otherwise more readily observedin the context of a diff pair. It will be understood that the terms“diff pair miRNA,” “biomarker miRNA,” and “comparative miRNA” are usedfor convenience and that embodiments discussed herein may or may notrefer to miRNAs using these terms. Regardless of whether the terms areused, the implementation of methods, kits, and other embodiments remainsessentially the same.

In further embodiments, methods involve comparing levels of expressionof different miRNAs in the pancreatic sample to each other or toexpression levels of other biomarkers, which occurs after a level ofexpression is measured or obtained. In certain embodiments, miRNAexpression levels are compared to each other. In some embodiments,methods involve comparing the level of expression of the at least onebiomarker miRNA to the level of expression of a comparative microRNA todetermine a biomarker diff pair value. A “comparative miRNA” refers to amiRNA whose expression level is used to evaluate the level of anothermiRNA in the sample; in some embodiments, the expression level of acomparative microRNA is used to evaluate a biomarker miRNA expressionlevel. For example, a differential value between the biomarker miRNA andthe comparative miRNA can be calculated or determined or evaluated; thisvalue is a number that is referred to as a “diff pair value” when it isbased on the expression level of two miRNAs. A diff pair value can becalculated, determined or evaluated using one or more mathematicalformulas or algorithms. In some embodiments, the value is calculated,determined or evaluated using computer software. Moreover, it is readilyapparent that the miRNA used as a biomarker and the miRNA used as thecomparative miRNA may be switched, and that any calculated value can beevaluated accordingly by a person of ordinary skill in the art. However,a person of ordinary skill in the art understands that different pairanalysis may be adjusted, particular with respect to altering thecomparative miRNA in a pair without affecting the concept of theembodiments discussed herein.

A comparative miRNA may be any miRNA, but in some embodiments, thecomparative miRNA is chosen because it allows a statisticallysignificant and/or relatively large difference in expression to bedetected or highlighted between expression levels of the biomarker inone pancreatic cyst population as compared to a different pancreaticcyst population. Furthermore, a particular comparative miRNA in a diffpair may serve to increase any difference observed between diff pairvalues of different pancreated cyst populations, for example, a PDACcell population compared to a CP cell population. In furtherembodiments, the comparative miRNA expression level serves as aninternal control for expression levels. In some embodiments, thecomparative miRNA is one that allows the relative or differential levelof expression of a biomarker miRNA to be distinguishable from therelative or differential level of expression of that same biomarker in adifferent pancreatic cyst population. In some embodiments, theexpression level of a comparative miRNA is a normalized level ofexpression for the different pancreatic cyst populations, while in otherembodiments, the comparative miRNA level is not normalized. In someembodiments, there are methods for distinguishing or identifyingpancreatic cancer cells in a patient comprising determining the level ofexpression of one or more miRNAs in a biological sample that containspancreatic cells from the patient.

Methods may involve determining the level of expression of one or moreof miR-135b, miR-148a, miR-24, miR-196a, miR-130b, miR-375, and/ormiR-96. It will be understood that “determining the level of expression”refers to measuring or assaying for expression of the recited microRNAusing a probe that is at least 98% complementary to the entire length ofthe mature human miRNA sequence, which will involve performing one ormore chemical reactions. In some embodiments, a probe that is at least99% or 100% complementary to the sequence of the entire length of themost predominant mature human miRNA sequence is used to implementembodiments discussed herein. It is contemplated that while additionalmiRNAs that are nearly identical to the recited miRNA may be measured inembodiments, the recited miRNA whose expression is being evaluated is atleast one of the miRNAs whose expression is being measured inembodiments. These different recited human miRNA sequences are providedin SEQ ID NOs: 1-12. Mature miRNAs may be indirectly determined bydirectly measuring precursor microRNA molecules; in some embodiments,this is done using the same probe that is used for measuring maturemiRNAs.

In some embodiments, there are methods for determining whether a patienthas pancreatic ductal carcinoma comprising: a) measuring from apancreatic sample from the patient the level of expression of at leasttwo of the following diff pair miRNAs: miR-135b, miR148a, miR-130b,miR-196a, miR-24, miR-375, miR-96, miR-155, miR-21, miR-24, miR-201,miR-217, miR-223, and miR-375, wherein at least one of the miRNAs is abiomarker miRNA and one is a comparative miRNA; b) determining at leastone biomarker diff pair value based on the level of expression of thebiomarker miRNA compared to the level of expression of the comparativemiRNA; and, c) evaluating whether the pancreatic sample comprisespancreatic ductal adenocarcinoma (PDAC) cells based on the biomarkerdiff pair value(s). In certain embodiments, the level of at least 3, 4,5, 6, 7, 8, 9, 10, 11, or 12 of the diff pair miRNAs are measured.

Embodiments involve 1, 2, 3, 4, 5 6, 7, 8, 9, 10, 11, or 12 probes thatare at least 90, 9, 92, 93, 94, 95, 96, 97, 98, 99, 01 100% identical orcomplementary to SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQID NO:5, SEQ ID NO:6, SEQ ID NO:7, SEQ ID NO:8, SEQ ID NO:9, SEQ IDNO:10, SEQ ID NO:11, or SEQ ID NO:12, depending on which miRNA is beingmeasured (see Table 1).

In other embodiments, methods involve measuring the level of miR-135bexpression in a pancreatic sample from a patient having aberrant orabnormal pancreatic cells; comparing the level of miR-135b expression tothe level of expression of miR-24 in the pancreatic sample; andproviding a score that provides information about the likelihood thatthe patient has pancreatic cancer cells. In further embodiments, methodsinvolve measuring the level of miR-148a expression in a pancreaticsample from a patient having aberrant or abnormal pancreatic cells;comparing the level of miR-148a expression to the level of expression ofmiR-135b in the pancreatic sample; and providing a score that providesinformation about the likelihood that the patient has pancreatic cancercells. In certain embodiments, methods are combined to evaluatedifferent miRNA pairs.

Some embodiments concern diagnosing a patient with PDAC after generatingan miRNA profile for a patient suspected of having or at risk for PDAC,wherein the miRNA profile comprises the level of expression of one ormore of miR-135b, miR-148a, miR-24, miR-196a, miR-130b, miR-375, and/ormiR-96. In other embodiments, an miRNA profile alternatively oradditionally comprises the level of expression of one or more ofmiR-155, miR-223, miR-217, miR-201 and/or miR-21. Such miRNAs may bereferred to as a “diff pair miRNA.” In some embodiments, the miRNA is abiomarker miRNA. In further embodiments, the miRNA is a comparativemiRNA.

In some embodiments, the level of miR-135b expression in a pancreaticsample from a patient is measured or assayed. In other embodiments, thelevel of miR-148a expression in a pancreatic sample from a patient ismeasured or assayed, which may be in addition to measuring or assayingfor miR-135b. In further embodiments, the level of miR-24 expression ina pancreatic sample from a patient is measured or assayed. In additionalembodiments, the level of miR-196a expression in a pancreatic samplefrom a patient is measured or assayed. In some embodiments, the level ofmiR-375 expression in a pancreatic sample from a patient is measured orassayed. In additional embodiments, the level of miR-96 expression in apancreatic sample from a patient is measured or assayed. It iscontemplated that methods may involve determining the level of 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11 or 12 different miRNAs or at least 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, or 12 different miRNAs, and any range derivabletherein. In specific embodiments, methods involve determining the levelof expression of at least or at most the following miRNAs: miR-135b,miR-148a, miR-24, miR-130b, and miR-196a. In further embodiments,methods involve determining the level of expression of at least or atmost the following miRNAs: miR-135b, miR-148a, miR-24. Alternatively oradditionally, methods involve determining the level of expression of oneor more of miR-155, miR-223, miR-217, miR-201 and/or miR-21. Methods mayor may not involve determining the amount or level of expression of anon-miRNA nucleic acid in sample.

Moreover, in some embodiments, methods involve evaluating a differentialpair analysis factor that involves one or more of the following pairs ofmiRNAs (the miRNA after the slash (/) is the reference miRNA):miR-135b/miR-24; miR-130b/miR-135b; miR135b/miR-148a; miR-375/miR-135b;miR-135b/miR-96; and miR-148a/miR-196a. In a specific embodiment,methods involve evaluating at least or at most the followingdifferential pair analysis factors: miR-135b/miR-24; miR-130b/miR-135b;miR135b/miR-148a; miR-375/miR-135b; miR-135b/miR-96; andmiR-148a/miR-196a; such pairs of miRNAs may also be referred to as “diffpairs.” In a different specific embodiment, methods involve evaluatingat least or at most the following differential pair analysis factors:125b/miR-24; miR-130b/miR-135b; miR-135b/miR-96; and miR-148a/miR-196a.In further embodiments, methods involve evaluating at least or at mostone or more of the following diff pairs: miR-155/miR-21, and/ormiR-130b/miR-24. However, a person of ordinary skill in the artunderstands that different pair analysis factors may be used, particularwith respect to altering the reference miRNA in a pair without affectingthe concept of the embodiments discussed herein. In some methods, thefollowing six diff pairs are evaluated: miR-135b/mir-24;miR-130b/miR-135b; miR-135b/miR-148a; miR-148a/miR-196a;miR-375/miR-135b; and miR-135b/miR-96. In further embodiments, these sixdiff pairs are evaluated, and further information about false positivesor false negatives is provided by evaluating the following diff pairs:miR-155/miR-21 and/or miR-130b/miR-24.

It is contemplated that 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 (or anyrange derivable therein) of the following diff pairs may be evaluatedand used in any embodiments discussed herein, including any method, anycomputer readable medium, any kit, or any computer processor:miR-21/miR-24; miR-21/miR-96; miR-21/miR-130b; miR-21/miR-135b;miR-21/miR-148a; miR-21/miR-155; miR-21/miR-196a; miR-21/miR-201;miR-21/miR-217; miR-21/miR-223; and/or miR-21/miR-375. At least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, or 11 of these diff pairs (or any range derivabletherein) may be used to evaluate a first risk score and/or they may beused to evaluate a second risk score, which may or may not be a reflextest. Such risk scores may be part of a linear analysis, a non-linearanalysis or a tree-based algorithm.

In certain embodiments, a sample is first evaluated using cytology, andonly if the sample is not characterized as PDAC by cytology is thesample then evaluated with respect to the level of expression of one ormore miRNAs, as discussed herein. In some cases, if the sample ischaracterized as benign, uncertain, pancreatitis or something other thanPDAC then the sample is evaluated for miRNA expression levels.

It is contemplated that 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 (or anyrange derivable therein) of the following diff pairs may be evaluatedand used in any embodiments discussed herein, including any method, anycomputer readable medium, any kit, or any computer processor:miR-24/miR-21; miR-24/miR-96; miR-24/miR-130b; miR-24/miR-135b;miR-24/miR-148a; miR-24/miR-155; miR-24/miR-196a; miR-24/miR-201;miR-24/miR-217; miR-24/miR-223; and/or miR-24/miR-375. At least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, or 11 of these diff pairs (or any range derivabletherein) may be used to evaluate a first risk score and/or they may beused to evaluate a second risk score, which may or may not be a reflextest. Such risk scores may be part of a linear analysis, a non-linearanalysis or a tree-based algorithm.

It is contemplated that 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 (or anyrange derivable therein) of the following diff pairs may be evaluatedand used in any embodiments discussed herein, including any method, anycomputer readable medium, any kit, or any computer processor:miR-96/miR-21; miR-96/miR-24; miR-96/miR-130b; miR-96/miR-135b;miR-96/miR-148a; miR-96/miR-155; miR-96/miR-196a; miR-96/miR-201; 217;miR-96/miR-223; and/or miR-96/miR-375. At least 1, 2, 3, 4, 5, 6, 7, 8,9, 10, or 11 of these diff pairs (or any range derivable therein) may beused to evaluate a first risk score and/or they may be used to evaluatea second risk score, which may or may not be a reflex test. Such riskscores may be part of a linear analysis, a non-linear analysis or atree-based algorithm.

It is contemplated that 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 (or anyrange derivable therein) of the following diff pairs may be evaluatedand used in any embodiments discussed herein, including any method, anycomputer readable medium, any kit, or any computer processor:miR-130b/miR-21; miR-130b/miR-24; miR-130b/miR-96; miR-130b/miR-135b;miR-130b/miR-148a; miR-130b/miR-155; miR-130b/miR-196a;miR-130b/miR-201; miR-130b/miR-217; miR-130b/miR-223; and/ormiR-130b/miR-375. At least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 of thesediff pairs (or any range derivable therein) may be used to evaluate afirst risk score and/or they may be used to evaluate a second riskscore, which may or may not be a reflex test. Such risk scores may bepart of a linear analysis, a non-linear analysis or a tree-basedalgorithm.

It is contemplated that 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 (or anyrange derivable therein) of the following diff pairs may be evaluatedand used in any embodiments discussed herein, including any method, anycomputer readable medium, any kit, or any computer processor:miR-135b/miR-21; miR-135b/miR-24; miR-135b/miR-96; miR-135b/miR-130b;miR-135b/miR-148a; miR-135b/miR-155; miR-135b/miR-196a;miR-135b/miR-201; miR-135b/miR-217; miR-135b/miR-223; and/ormiR-135b/miR-375. At least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 of thesediff pairs (or any range derivable therein) may be used to evaluate afirst risk score and/or they may be used to evaluate a second riskscore, which may or may not be a reflex test. Such risk scores may bepart of a linear analysis, a non-linear analysis or a tree-basedalgorithm.

It is contemplated that 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 (or anyrange derivable therein) of the following diff pairs may be evaluatedand used in any embodiments discussed herein, including any method, anycomputer readable medium, any kit, or any computer processor:miR-148a/miR-21; miR-148a/miR-24; miR-148a/miR-96; miR-148a/miR-130b;miR-148a/miR-135b; miR-148a/miR-155; miR-148a/miR-196a;miR-148a/miR-201; miR-148a/miR-217; miR-148a/miR-223 and/ormiR-148a/miR-375. At least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 of thesediff pairs (or any range derivable therein) may be used to evaluate afirst risk score and/or they may be used to evaluate a second riskscore, which may or may not be a reflex test. Such risk scores may bepart of a linear analysis, a non-linear analysis or a tree-basedalgorithm.

It is contemplated that 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 (or anyrange derivable therein) of the following diff pairs may be evaluatedand used in any embodiments discussed herein, including any method, anycomputer readable medium, any kit, or any computer processor:miR-155/miR-21; miR-155/miR-24; miR-155/miR-96; miR-155/miR-130b;miR-155/miR-135b; miR-155/miR-148a; miR-155/miR-196a; miR-155/miR-201;miR-155/miR-217; miR-155/miR-223 and/or miR-155/miR-375. At least 1, 2,3, 4, 5, 6, 7, 8, 9, 10, or 11 of these diff pairs (or any rangederivable therein) may be used to evaluate a first risk score and/orthey may be used to evaluate a second risk score, which may or may notbe a reflex test. Such risk scores may be part of a linear analysis, anon-linear analysis or a tree-based algorithm.

It is contemplated that 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 (or anyrange derivable therein) of the following diff pairs may be evaluatedand used in any embodiments discussed herein, including any method, anycomputer readable medium, any kit, or any computer processor:miR-196a/miR-21; miR-196a/miR-24; miR-196a/miR-96; miR-196a/miR-130b;miR-196a/miR-135b; miR-196a/miR-148a; miR-196a/miR-155;miR-196a/miR-201; miR-196a/miR-217; miR-196a/miR-223 and/ormiR-196a/miR-375. At least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 of thesediff pairs (or any range derivable therein) may be used to evaluate afirst risk score and/or they may be used to evaluate a second riskscore, which may or may not be a reflex test. Such risk scores may bepart of a linear analysis, a non-linear analysis or a tree-basedalgorithm.

It is contemplated that 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 (or anyrange derivable therein) of the following diff pairs may be evaluatedand used in any embodiments discussed herein, including any method, anycomputer readable medium, any kit, or any computer processor:miR-201/miR-21; miR-201/miR-24; miR-201/miR-96; miR-201/miR-130b;miR-201/miR-135b; miR-201/miR-148a; miR-201/miR-155; miR-201/miR-196a;miR-201/miR-217; miR-201/miR-223 and/or miR-201/miR-375. At least 1, 2,3, 4, 5, 6, 7, 8, 9, 10, or 11 of these diff pairs (or any rangederivable therein) may be used to evaluate a first risk score and/orthey may be used to evaluate a second risk score, which may or may notbe a reflex test. Such risk scores may be part of a linear analysis, anon-linear analysis or a tree-based algorithm.

It is contemplated that 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 (or anyrange derivable therein) of the following diff pairs may be evaluatedand used in any embodiments discussed herein, including any method, anycomputer readable medium, any kit, or any computer processor:miR-217/miR-21; miR-217/miR-24; miR-217/miR-96; miR-217/miR-130b;miR-217/miR-135b; miR-217/miR-148a; miR-217/miR-155; miR-217/miR-196a;miR-217/miR-201; miR-217/miR-223 and/or miR-217/miR-375. At least 1, 2,3, 4, 5, 6, 7, 8, 9, 10, or 11 of these diff pairs (or any rangederivable therein) may be used to evaluate a first risk score and/orthey may be used to evaluate a second risk score, which may or may notbe a reflex test. Such risk scores may be part of a linear analysis, anon-linear analysis or a tree-based algorithm.

It is contemplated that 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 (or anyrange derivable therein) of the following diff pairs may be evaluatedand used in any embodiments discussed herein, including any method, anycomputer readable medium, any kit, or any computer processor:miR-223/miR-21; miR-223/miR-24; miR-223/miR-96; miR-223/miR-130b;miR-223/miR-135b; miR-223/miR-148a; miR-223/miR-155; miR-223/miR-196a;miR-223/miR-201; miR-223/miR-217 and/or miR-223/miR-375. At least 1, 2,3, 4, 5, 6, 7, 8, 9, 10, or 11 of these diff pairs (or any rangederivable therein) may be used to evaluate a first risk score and/orthey may be used to evaluate a second risk score, which may or may notbe a reflex test. Such risk scores may be part of a linear analysis, anon-linear analysis or a tree-based algorithm.

It is contemplated that 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 (or anyrange derivable therein) of the following diff pairs may be evaluatedand used in any embodiments discussed herein, including any method, anycomputer readable medium, any kit, or any computer processor:miR-375/miR-21; miR-375/miR-24; miR-375/miR-96; miR-375/miR-130b;miR-375/miR-135b; miR-375/miR-148a; miR-375/miR-155; miR-375/miR-196a;miR-375/miR-201; miR-375/miR-217; and/or miR-375/miR-223. At least 1, 2,3, 4, 5, 6, 7, 8, 9, 10, or 11 of these diff pairs (or any rangederivable therein) may be used to evaluate a first risk score and/orthey may be used to evaluate a second risk score, which may or may notbe a reflex test. Such risk scores may be part of a linear analysis, anon-linear analysis or a tree-based algorithm.

In certain embodiments, miR-135b expression levels are used to calculate1, 2, 3, 4, 5, 6, 7 or more differential pair analysis factors (or anyrange derivable therein). In specific embodiments, miR-135b expressionlevels are used to calculate up to five differential pair analysisfactors. In further embodiments, miR-148a expression levels are used tocalculate 1, 2, 3, 4, 5, 6, 7 or more differential pair analysis factors(or any range derivable therein). In specific embodiments, miR-148aexpression levels are used to calculate up to two differential pairanalysis factors. In certain embodiments, miR-130b expression levels areused to calculate 1, 2, 3, 4, 5, 6, 7 or more differential pair analysisfactors (or any range derivable therein). In certain embodiments,miR-196a expression levels are used to calculate 1, 2, 3, 4, 5, 6, 7 ormore differential pair analysis factors (or any range derivabletherein). In certain embodiments, miR-24 expression levels are used tocalculate 1, 2, 3, 4, 5, 6, 7 or more differential pair analysis factors(or any range derivable therein). In certain embodiments, miR-375expression levels are used to calculate 1, 2, 3, 4, 5, 6, 7 or moredifferential pair analysis factors (or any range derivable therein). Incertain embodiments, miR-96 expression levels are used to calculate 1,2, 3, 4, 5, 6, 7 or more differential pair analysis factors (or anyrange derivable therein).

The level of expression of any of these microRNAs may be used tocalculate or assess its relative or differential level of expression asa biomarker for PDAC by comparing its level of expression to the levelof expression of a reference miRNA. A reference miRNA may be any miRNA,but in some embodiments, the reference miRNA is one that allows astatisitically significant and/or relatively large difference inexpression to be detected between expression levels of the biomarker inone pancreatic cell population as compared to a different pancreaticcell population. In further embodiments, the reference miRNA expressionlevel serves as an internal control for expression levels. In someembodiments, the reference miRNA is one that allows the relative ordifferential level of expression of a PDAC biomarker to bedistinguishable from the relative or differential level of expression ofthat same biomarker in a non-PDAC pancreatic cell. In some embodiments,the expression level of a reference miRNA is a normalized level ofexpression for the different pancreatic cell populations, while in otherembodiments, the reference miRNA level is not normalized.

In some embodiments, an miRNA whose expression is used as a biomarkermay also be used as a reference miRNA. Therefore, in certainembodiments, the level of expression each of these microRNAs (miR-135b,miR-148a, miR-24, miR-196a, miR-130b, miR-375, miR-96, miR-21, miR-155,miR-201, miR-217, miR-223, and miR-375) may be used insead of or also asa reference level of expression that can be used to calculate or assessa relative or differential level of expression. For example, in someembodiments, miR-135b is used a reference miRNA against which therelative or differential level of expression of a different miRNA isdetermined.

Some methods are provided for determining whether a patient haspancreatic ductal carcinoma comprising: a) measuring from a pancreaticsample from the patient the level of expression of at least one of thefollowing diff pair miRNAs: miR-135b, miR148a, miR-130b, miR-196a,miR-24, miR-375, or miR-96, wherein at least one of the miRNAs is abiomarker miRNA; b) comparing the level of expression of the at leastone biomarker miRNA to the level of expression of at least onecomparative microRNA to calculate at least one biomarker diff pairvalue; and, c) determining the pancreatic sample comprises pancreaticductal adenocarcinoma (PDAC) cells based on the biomarker diff pairvalue(s).

Other embodiments concern methods for evaluating a pancreatic samplefrom a patient comprising: a) measuring from the pancreatic sample fromthe patient the level of expression of at least miR-135b and at leastone comparative miRNA; b) comparing the level of expression betweenmiR-135b and the second microRNA to calculate a miR-135b diff pairvalue; and, c) calculating the patient's risk of having pancreaticductal carcinoma using the miR-135b diff pair value.

Further embodiments involve methods for evaluating a pancreatic samplefrom a patient comprising: a) from the sample, measuring the level ofexpression of miRNAs from at least two diff pairs selected from thegroup consisting of miR-135b/mir-24; miR-130b/miR-135b;miR-135b/miR-148a; miR-148a/miR-196a; miR-375/miR-135b; miR-135b/miR-96;miR-155/miR-21 and miR-130b/miR-24; b) determining diff pair values forthe at least two diff pairs; and, c) calculating a risk score forpancreatic ductal adenocarcinoma for the patient.

In further embodiments, methods involve calculating a differential orrelative value of the expression level of a particular miRNA and thelevel of expression of a reference miRNA in the sample, wherein thedifferential or relative value is a factor, termed differential pairanalysis factor, that may be evaluated with one or more otherdifferential pair analysis factors (i.e., involving different pairwisecomparisons). In some embodiments, methods include evaluating one ormore differential pair analysis factors using a scoring algorithm togenerate a risk score for the presence of pancreatic cancer cells in thepancreatic sample, wherein the patient is identified as having or as nothaving pancreatic cancer cells based on the score. It is understood bythose of skill in the art that the score is a predictive value aboutwhether the patient does or does not pancreatic cells. In someembodiments, a report is generated and/or provided that identifies therisk score.

Additionally, some methods involve evaluating or determining a firstrisk score, and then in some embodiments, evaluating or determining asecond, third and/or fourth risk score. The first and additional riskscores may be evaluated or determined at the same time, or the firstscore may be determined or evaluated first, and then one or more otherscores may be determined or evaluated. In some embodiments, a subsequentscore is determined or evaluated depending on the first risk score. Forinstance, in some embodiments, a first risk score may indicate that thepatient is positive for PDAC, and then subsequently or concurrently, asecond risk score may be evaluated based on one or more miRNA expressionlevels and/or diff pairs or diff pair values discussed herein todetermined whether the first risk score reflects a true positive or afalse positive. In some embodiments, more than one risk score isevaluated. In certain cases, at least one risk score is determined orevaluated. In further embodiments, there is a second risk score that isbased on the expression level of miR-135b, miR-148a, miR-24, miR-196a,miR-130b, miR-375, and/or miR-96 alone or as a diff pair along with theexpression of a different miRNA that is chosen from miR-135b, miR-148a,miR-24, miR-196a, miR-130b, miR-375, and/or miR-96. In some embodiments,a second risk score involves measuring the level of expression of one ormore of miR-155, miR-223, miR-217, miR-201 and/or miR-21. In someembodiments, false negatives and/or false positives are furtherevaluated using a reflex test. Alternatively or additional, risk scoresrelating to false positives and/or false negatives may be part of alinear analysis, a non-linear analysis or a tree-based algorithm.

In some embodiments, a cut-off score is employed to characterize asample as likely having PDAC cells or not having PDAC cells. In someembodiments, the risk score for the patient is compared to a cut-offscore to characterize the biological sample from the patient withrespect to the presence of PDAC cells.

In some embodiments, the level of expression of an miRNA biomarker suchas miR-1235b, miR148, miR130b, miR-196a, miR-24, miR-375, or miR-96 maybe discussed as being upregulated or downregulated in a PDAC cellcompared to a non-pDAC cell, however, a person of ordinary skill in theart will understand that the embodiments herein focus on relativepairwise values in order to provide increased clarity for differentialexpression between pancreatic cancer cells and non-cancer pancreaticcells. Nonethless, embodiments may be implemented with respect to a 1,2, 3, 4, 5, 6, 7, 8, 9, or 10-fold difference (increase or decrease, asseen in the Examples) between PDAC cells and pancreatitis or non-PDACcells.

In some embodiments, methods further comprise one or more of thefollowing: comparing the level of miR-135b to the level of miR-148aexpression in the biological sample; comparing the level of miR-135b tothe level of miR-96 expression in the biological sample; comparing thelevel of miR-135b to the level of miR-96 expression in the biologicalsample; comparing the level of miR-130b to the level of miR-135bexpression in the biological sample; comparing the level of miR-375 tothe level of miR-135b expression in the biological sample; or comparingthe level of miR-148a to the level of miR-196a expression in thebiological sample. In certain embodiments, the level of miR-210expression is not determined or the level of miR-196a expression is notcompared to the level of miR-135b expression in the biological sample.

In certain methods, the patient has already been identified as possiblyhaving pancreatic cancer based on the detection or observation ofaberrant pancreatic cells. Thus, in some embodiments, a biologicalsample comprising pancreatic cells has already been obtained directlyfrom the patient. In additional embodiments, methods involve obtainingfrom the patient, e.g., retrieving from the patient, a biological samplethat contains pancreatic cells (“pancreatic biological sample” or“pancreatic sample”). In some embodiments, a first, second, or thirdbiological sample comprising pancreatic cells is obtained directly orretrieved from the patient. In further embodiments, methods involveobtaining a patient's biological sample, which may or may not involveretrieving the sample from the patient. For example, a clinician maydirectly obtain (or retrieve) the sample from the patient. An entitythat will assay the sample may obtain the patient's sample from theclinician who retrieved the patient's sample.

The biological sample used in embodiments is obtained so as to retrievefrom the patient pancreatic cells. In some embodiments, the biologicalsample is a microdissected sample, while in other embodiments, thebiological sample is a macrodissected sample. In certain embodiments,methods involve obtaining a biological sample with a fine needleaspirate (FNA). Alternatively, methods may involve a biological sampleretrieved from a biopsy, such as a fine needle aspiration biopsy (FNAB)or needle aspiration biopsy (NAB). In certain embodiments, methodsinvolve a biological sample that is a formalin-fixed paraffin embedded(PPFE) sample.

Some embodiments further involve isolating ribonucleic or RNA from abiological sample. Other steps may or may not include amplifying anucleic acid in a sample and/or hybridizing one or more probes to anamplified or non-amplified nucleic acid. In certain embodiments, amicroarray may be used to measure or assay the level of miRNA expressionin a sample.

The term “miRNA” is used according to its ordinary and plain meaning andrefers to a microRNA molecule found in eukaryotes that is involved inRNA-based gene regulation. See, e.g., Carrington et al., 2003, which ishereby incorporated by reference. The term will be used to refer to thesingle-stranded RNA molecule processed from a precursor. IndividualmiRNAs have been identified and sequenced in different organisms, andthey have been given names. Names of miRNAs that are related to thedisclosed methods and compositions, as well as their sequences, areprovided herein. The name of the miRNAs that are used in methods andcompositions refers to an miRNA that is at least 90% identical to thenamed miRNA based on its matured sequence listed herein and that iscapable of being detected under the conditions described herein usingthe designated ABI part number for the probe. In most embodiments, thesequence provided herein is the sequence that is being measured inmethods described herein. In some methods, a step may involving using anucleic acid with the sequence comprising or consisting of any of SEQ IDNOs:1-11 to measure expression of a miRNA in the sample. In someembodiments, a complement of SEQ ID NO:1 (UAGGUAGUUUCAUGUUGUUGG) is usedto measure expression of naturally occurring miR-196a in a sample. Inother embodiments, a complement of SEQ ID NO:2 (CUGUGCGUGUGACAGCGGCUGA)is used to measure expression of naturally occurring miR-201 in asample. In further embodiments, a complement of SEQ ID NO:3(UACUGCAUCAGGAACUGAUUGGA) is used to measure expression of naturallyoccurring miR-217. In further embodiments, a complement of SEQ ID NO:4(UUUGUUCGUUCGGCUCGCGUGA) is used to measure expression of naturallyoccurring miR-375. In other embodiments, a complement of SEQ ID NO:5(CAGUGCAAUGAUGAAAGGGCAU) is used to measure expression of naturallyoccurring miR-130. In some embodiments, a complement of SEQ ID NO:6(UAUGGCUUUUCAUUCCUAUGUG) is used to measure expression of naturallyoccurring miR-135b. In other embodiments, a complement of SEQ ID NO:7(UCAGUGCACUACAGAACUUUGU) is used to measure expression of naturallyoccurring miR148a. In additional embodiments, a complement of SEQ IDNO:8 (UUAAUGCUAAUCGUGAUAGGGG) is used to measure expression of naturallyoccurring miR-155. In further embodiments, a complement of SEQ ID NO:9(UGUCAGUUUGUCAAAUACCCC) is used to measure the expression of naturallyoccurring miR-223. In other embodiments, a complement of SEQ ID NO:10(UUUGGCACUAGCACAUUUUUGC) is used to measure the expression of naturallyoccurring miR-96. In certain embodiments, a complement of SEQ ID NO:11(UGGCUCAGUUCAGCAGGAACAG) is used to measure the expression of naturallyoccurring miR-24. In other embodiments, a complement of SEQ ID NO:12(UAGCUUAUCAGACUGAUGUUGA) is used to measure the expression of naturallyoccurring miR-21.

The term “naturally occurring” refers to something found in an organismwithout any intervention by a person; it could refer to anaturally-occurring wildtype or mutant molecule. In some embodiments asynthetic miRNA molecule, such as aprobe or primer, does not have thesequence of a naturally occurring miRNA molecule. In other embodiments,a synthetic miRNA molecule may have the sequence of a naturallyoccurring miRNA molecule, but the chemical structure of the moleculethat is unrelated specifically to the precise sequence (i.e.,non-sequence chemical structure) differs from chemical structure of thenaturally occurring miRNA molecule with that sequence. CorrespondingmiRNA sequences that can be used in the context of the disclosed methodsand compositions include, but are not limited to, all or a portion ofthose sequences in the SEQ ID NOs disclosed herein, as well as any othermiRNA sequence, miRNA precursor sequence, or any sequence complementarythereof. In some embodiments, the sequence is or is derived from orcontains all or part of a sequence identified herein to target aparticular miRNA (or set of miRNAs) that can be used with that sequence.

In some embodiments, it may be useful to know whether a cell expresses aparticular miRNA endogenously or whether such expression is affectedunder particular conditions or when the organism is in a particulardisease state. Thus, in some embodiments, methods include assaying acell or a sample containing a cell for the presence of one or moremiRNAs. Consequently, in some embodiments, methods include a step ofgenerating a miRNA profile for a sample. The term “miRNA profile” refersto a set of data regarding the expression pattern for a plurality ofmiRNAs (e.g., one or more miRNAs disclosed herein) in the sample; it iscontemplated that the miRNA profile can be obtained using a set ofmiRNAs, using for example nucleic acid amplification or hybridizationtechniques well know to one of ordinary skill in the art.

In some embodiments, a miRNA profile is generated by steps that include:(a) labeling miRNA in the sample; b) hybridizing miRNA to a number ofprobes, or amplifying a number of miRNAs, and c) determining miRNAhybridization to the probes or detection of miRNA amplificationproducts, wherein a miRNA profile is generated. See, e.g., U.S.Provisional Patent Application No. 60/575,743; U.S. Provisional PatentApplication No. 60/649,584; and U.S. patent application Ser. No.11/141,707, all of which are hereby incorporated by reference. One miRNAmay be evaluated at a time or measurements may be done simultaneously.In some embodiments, reactions are multiplexed in order to measure thelevel of expression of more than one miRNA.

Some methods involve diagnosing a patient based on a miRNA expressionprofile. In certain embodiments, the elevation or reduction in the levelof expression of a particular miRNA or set of miRNAs in a cell iscorrelated with a disease state, as compared to the expression level ofthat miRNA or set of miRNAs in a normal cell. This correlation allowsfor diagnostic methods to be carried out when that the expression levelof a miRNA is measured in a biological sample and then compared to theexpression level of a normal sample. Similarly, a set of miRNAs may bemeasured in a biological sample and then compared to the expressionlevels of those miRNAs in a normal sample. Also, a ratio (or ratios) orone or more miRNAs as compared to one or more other microRNAs may bedetermined in a biological sample and then compared to the correspondingratio (or ratios) determined for a normal sample. It is specificallycontemplated that miRNA profiles for patients, particularly thosesuspected of having a disease or condition such as pancreatits orpancreatic cancer, can be generated by evaluating any of or sets of themiRNAs discussed in this disclosure. The miRNA profile that is generatedfrom the patient will be one that provides information regarding theparticular disease or condition. In many embodiments, the miRNA profileis generated using miRNA hybridization or amplification, (e.g., arrayhybridization or RT-PCR). In certain aspects, a miRNA profile can beused in conjunction with other diagnostic tests, such as proteinprofiles in the serum or cytopathology examination.

Embodiments include methods for diagnosing and/or assessing a conditionin a patient comprising measuring an expression profile of one or moremiRNAs in a sample from the patient. The difference in the expressionprofile in the sample from the patient and a reference expressionprofile (such as an expression profile from a normal, non-pathologic,non-cancerous sample) is indicative of a pathologic, disease, orcancerous condition. A miRNA or probe set comprising or identifying asegment of a corresponding miRNA can include all or part of 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,90, 95, 100, 125, 150, 175, 200, 250, 300, 350, or any integer or rangederivable there between, of a miRNA or a probe disclosed herein. It iscontemplated that methods may involve a microarray or that compositionsmay involve a microarray comprising one or more miRNA probe setsdiscussed herein.

A sample may be taken from a patient having or suspected of having adisease or pathological condition. In certain aspects, the sample canbe, but is not limited to a tissue (e.g., biopsy, such as fine needlebiopsy), blood, serum, plasma, or pancreatic juice sample. The samplemay be fresh, frozen, fixed (e.g., formalin fixed), or embedded (e.g.,paraffin embedded). In a particular aspect, the sample can be apancreatic sample.

The disclosed methods can be used to diagnose or assess a pathologicalcondition. In a certain aspect, the condition is a non-cancerouscondition, such as pancreatitis or chronic pancreatitis. In otheraspects, the condition is a cancerous condition, such as pancreaticcancer, and in particular aspects, the cancerous condition is pancreaticductal adenocarcinoma (PDAC).

Certain embodiments include determining the expression of one or moremiRNAs by using an amplification assay or a hybridization assay, avariety of which are well known to one of ordinary skill in the art. Incertain aspects, an amplification assay can be a quantitativeamplification assay, such as quantitative RT-PCR or the like. In stillfurther aspects, a hybridization assay can include array hybridizationassays or solution hybridization assays.

In certain aspects, methods and compositions are provided to diagnose orassess a patient's condition. For example, the methods can be used toscreen for a pathological condition, assess prognosis of a pathologicalcondition, stage a pathological condition, or assess response of apathological condition to therapy. In further embodiments, methodsinclude identifying or selecting a patient for treatment of PDAC ortreating a patient for PDAC. Additional steps include monitoring apatient or not treating a patient for PDAC. In specific embodiments, apatient is determined not to have or be at risk for PDAC, in which casethe patient is not treated for cancer but monitored for changes innon-cancer status.

Some embodiments concern nucleic acids that, when introduced into cells,perform the activities of or inhibit endogenous miRNAs. In certainaspects, nucleic acids are synthetic or non-synthetic miRNAs.Sequence-specific miRNA inhibitors can be used to inhibit the activitiesof one or more endogenous miRNAs in cells, as well those genes andassociated pathways modulated by the endogenous miRNA. Such miRNAs maybe used sequentially or in combination. When miRNAs are used to inhibitactivities of endogenous miRNAs, the inhibition of such activities maybe sequential or in combination.

In some embodiments, short nucleic acid molecules function as miRNAs oras inhibitors of miRNAs in a cell. The term “short” refers to a lengthof a single polynucleotide that is 25, 50, 100, or 150 nucleotides orfewer, including all integers or ranges derivable there between.

Such nucleic acid molecules may be synthetic and isolated. While in someembodiments, nucleic acids do not have an entire sequence that isidentical to a sequence of a naturally-occurring nucleic acid, suchmolecules may encompass all or part of a naturally-occurring sequence.It is contemplated, however, that a synthetic nucleic acid administeredto a cell may subsequently be modified or altered in the cell such thatits structure or sequence is the same as non-synthetic or naturallyoccurring nucleic acid, such as a mature miRNA sequence. For example, asynthetic nucleic acid may have a sequence that differs from thesequence of a precursor miRNA, but that sequence may be altered once ina cell to be the same as an endogenous, processed miRNA. Nucleic acidmolecules are “isolated” when the nucleic acid molecules are initiallyseparated from different (in terms of sequence or structure) andunwanted nucleic acid molecules such that a population of isolatednucleic acids is at least about 90% homogenous, and may be at leastabout 95, 96, 97, 98, 99, or 100% homogenous with respect to otherpolynucleotide molecules. In many embodiments, a nucleic acid isisolated by virtue of it having been synthesized in vitro separate fromendogenous nucleic acids in a cell. It will be understood, however, thatisolated nucleic acids may be subsequently mixed or pooled together.

In some embodiments, there is a synthetic miRNA having a length ofbetween 17 and 130 residues. The disclosed methods and compositions mayconcern synthetic miRNA molecules that are, are at least, or are at most15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86,87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 140,145, 150, 160, 170, 180, 190, 200 or more residues in length, includingany integer or any range derivable therein.

In certain embodiments, synthetic miRNAs used as probes have a“complementary region” having a sequence from 5′ to 3′ is between 60%and 100% complementary to the miRNA sequence. In certain embodiments,these synthetic miRNAs are also isolated, as defined above. The term“complementary region” refers to a region of a synthetic miRNA that isor is at least 60% complementary to a paricular mature, naturallyoccurring miRNA sequence. The complementary region is or is at least 60,61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96,97, 98, 99, 99.1, 99.2, 99.3, 99.4, 99.5, 99.6, 99.7, 99.8, 99.9 or 100%complementary, or any range derivable therein. In some embodiments,there may be a hairpin loop structure.

Furthermore, any method articulating a list of miRNAs using Markushgroup language may be articulated without the Markush group language anda disjunctive article (i.e., or) instead, and vice versa.

The methods may further comprise administering an anticancer therapyafter a patient is determined to have a risk score that indicates asignificant likelihood that the patient has pancreatic cancer. This maybe done in conjunction with a cytopathology analysis or evaluation thatindicates or confirms the patient has or likely has pancreatic cancer.The anticancer therapy can be, but is not limited to, chemotherapy,radiotherapy, surgery, or immunotherapy. A person of ordinary skill inthe art would know the appropriate therapy for PDAC. In otherembodiments, a patient is determined not to have PDAC. In some cases,the patient is determined to instead have chronic pancreatitis (CP),which may be subsequently treated. Therefore, in some embodiments apatient is treated for chronic pancreatitis after miRNAs have beenmeasured and analyzed as discussed herein.

In some embodiments, a score involves weighting the a diff pair value.In some embodiments, one or more of the following diff pairs is erightedin order to increase or decrease the significance of that diff pair incalculating a risk score: miR-135b/mir-24; miR-130b/miR-135b;miR-135b/miR-148a; miR-148a/miR-196a; miR-375/miR-135b; miR-135b/miR-96;miR-155/miR-21 or miR-130b/miR-24. In certain embodiments, the weightingranges from the following numbers or is at least or at most 0.01, 0.02,0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6,0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0,2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.1, 3.2, 3.3, 3.4,3.5, 3.6, 3.7. 3.8, 3.9, 4.0, 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8,4.9, 5.0, or any range derivable therein.

It will be understood that the term “providing” an agent is used toinclude “administering” the agent to a patient.

Also provided are kits containing the disclosed compositions orcompositions used to implement the disclosed methods. In someembodiments, kits can be used to evaluate one or more miRNA molecules.In certain embodiments, a kit contains, contains at least or contains atmost 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 or more, or any rangeand combination derivable therein, of miRNA probes, synthetic miRNAmolecules, or miRNA inhibitors. In some embodiments, there are kits forevaluating miRNA activity in a cell.

Kits may comprise components, which may be individually packaged orplaced in a container, such as a tube, bottle, vial, syringe, or othersuitable container.

Individual components may also be provided in a kit in concentratedamounts; in some embodiments, a component is provided individually inthe same concentration as it would be in a solution with othercomponents. Concentrations of components may be provided as 1×, 2×, 5×,10×, or 20× or more, or any range derivable therein.

Kits for using miRNA probes, synthetic miRNAs, nonsynthetic miRNAs,and/or miRNA inhibitors for therapeutic, prognostic, or diagnosticapplications are provided. Specifically contemplated are any suchmolecules corresponding to any miRNA reported to influence biologicalactivity, such as those discussed herein.

In certain aspects, negative and/or positive control synthetic miRNAsand/or miRNA inhibitors are included in some kit embodiments. Suchcontrol molecules can be used, for example, to verify transfectionefficiency and/or control for transfection-induced changes in cells.

It is contemplated that any method or composition described herein canbe implemented with respect to any other method or composition describedherein, and that different embodiments may be combined. It isspecifically contemplated that any methods and compositions discussedherein with respect to miRNA molecules may be implemented with respectto synthetic miRNAs to the extent the synthetic miRNA is exposed to theproper conditions to allow it to become a mature miRNA underphysiological circumstances.

Any embodiment involving specific miRNAs is contemplated also to coverembodiments involving miRNAs whose sequences are at least 80, 81, 82,83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99%identical to the mature sequence of the specified miRNA or to involve 1,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 or more (or any range derivabletherein) miRNA probes whose sequences are at least 80, 81, 82, 83, 84,85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99%complementary to the mature sequence of the specified miRNA. In otherembodiments, embodiments may involve 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,or 12 or more (or any range derivable therein) miRNA probes, which maybe capable of specifically detecting any of the following miRNAs:miR-196a, miR-210, miR-217, miR-375, miR-130, miR-135b, miR-148a,miR-155, miR-223, miR-96, miR-24, and/or miR-21.

In some embodiments, methods will involve determining or calculating adiagnostic or risk score based on data concerning the expression levelof one or more miRNAs, meaning that the expression level of the one ormore miRNAs is at least one of the factors on which the score is based.A diagnostic or risk score will provide information about the biologicalsample, such as the general probability that the pancreatic samplecontains PDAC cells or that the pancreatic sample does not contain PDACcells. In some embodiments, the diagnostic or risk score represents theprobability that the patient is more likely than not to have PDAC. Inother embodiments, the diagnostic or risk score represents theprobability that the patient has benign cells or chronic pancreaticcells or non-PDAC cancer cells. In certain embodiments, a probabilityvalue is expressed as a numerical integer that represents a probabilityof 0% likelihood to 100% likelihood that a patient has PDAC or does nothave PDAC (or has benign cells or normal cells or CP cells or some othertype of cancer cells). In some embodiments, the probability value isexpressed as a numerical integer that represents a probability of 0, 1,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57,58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75,76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93,94, 95, 96, 97, 98, 99, or 100% likelihood (or any range derivabletherein) that a patient has PDAC or something other than PDAC.

In some embodiments, methods include evaluating one or more differentialpair values using a scoring algorithm to generate a diagnostic or riskscore for having PDAC, wherein the patient is identified as having or asnot having such a based on the score. It is understood by those of skillin the art that the score is a predictive value about whether thepatient does or does not s have PDAC. In some embodiments, a report isgenerated and/or provided that identifies the diagnostic score or thevalues that factor into such a score. In some embodiments, a cut-offscore is employed to characterize a sample as likely having PDAC (oralternatively not having PDAC). In some embodiments, the risk score forthe patient is compared to a cut-off score to characterize thebiological sample from the patient with respect to whether they arelikely to have or not to have PDAC.

Any of the methods described herein may be implemented on tangiblecomputer-readable medium comprising computer-readable code that, whenexecuted by a computer, causes the computer to perform one or moreoperations. In some embodiments, there is a tangible computer-readablemedium comprising computer-readable code that, when executed by acomputer, causes the computer to perform operations comprising: a)receiving information corresponding to a level of expression in apancreatic or pancreas sample from a patient of at least one, two, orthree of the following miRNAs: miR-135b, miR-148a, miR-24, miR-196a,miR-130b, miR-375, and/or miR-96; and b) determining a biomarker diffpair value using information corresponding to the at least one biomarkermiRNA and information corresponding to the level of expression of acomparative miRNA. The diff pair value or a combination of diff pairvalues provide information that allows a risk score for PDAC to bedetermined. In some embodiments, receiving information comprisesreceiving from a tangible data storage device information correspondingto a level of expression in a pancreatic sample from a patient of atleast two of the following diff pair miRNAs: miR-135b, miR-148a,miR-130b, miR-196a, miR-24, miR-375, or miR-96, wherein at least one ofthe miRNAs is a biomarker miRNA. In additional embodiments the mediumfurther comprises computer-readable code that, when executed by acomputer, causes the computer to perform one or more additionaloperations comprising: sending information corresponding to thebiomarker diff pair value to a tangible data storage device. In specificembodiments, it further comprises computer-readable code that, whenexecuted by a computer, causes the computer to perform one or moreadditional operations comprising: sending information corresponding tothe biomarker diff pair value to a tangible data storage device. Incertain embodiments, receiving information comprises receiving from atangible data storage device information corresponding to a level ofexpression in a pancreatic sample from a patient of at least two of thefollowing diff pair miRNAs: miR-135b, miR-148a, miR-130b, miR-196a,miR-24, miR-375, or miR-96, wherein at least one of the miRNAs is abiomarker miRNA. In even further embodiments, the tangiblecomputer-readable medium has computer-readable code that, when executedby a computer, causes the computer to perform operations furthercomprising: c) calculating a risk score for the pancreatic sample,wherein the risk score is indicative of the probability that thepancreatic sample contains PDAC cells or that the patient has PDAC. Inparticular embodiments, methods or computer readable code allow theimplementation of one or more scoring algorithms. In some cases, thescoring algorithm comprises a method selected from the group consistingof: Linear Discriminate Analysis (LDA), Significance Analysis ofMicroarrays, Tree Harvesting, CART, MARS, Self Organizing Maps, FrequentItem Set, Bayesian networks, Prediction Analysis of Microarray (PAM),SMO, Simple Logistic Regression, Logistic Regression, MultilayerPerceptron, Bayes Net, Naive Bayes, Naive Bayes Simple, Naive Bayes Up,IB1, Ibk, Kstar, LWL, AdaBoost, ClassViaRegression, Decorate, MulticlassClassifier, Random Committee, j48, LMT, NBTree, Part, Random Forest,Ordinal Classifier, Sparse Linear Programming (SPLP), Sparse LogisticRegression (SPLR), Elastic NET, Support Vector Machine, Prediction ofResidual Error Sum of Squares (PRESS), and combinations thereof.

A processor or processors can be used in performance of the operationsdriven by the example tangible computer-readable media disclosed herein.Alternatively, the processor or processors can perform those operationsunder hardware control, or under a combination of hardware and softwarecontrol. For example, the processor may be a processor specificallyconfigured to carry out one or more those operations, such as anapplication specific integrated circuit (ASIC) or a field programmablegate array (FPGA). The use of a processor or processors allows for theprocessing of information (e.g., data) that is not possible without theaid of a processor or processors, or at least not at the speedachievable with a processor or processors. Some embodiments of theperformance of such operations may be achieved within a certain amountof time, such as an amount of time less than what it would take toperform the operations without the use of a computer system, processor,or processors, including no more than one hour, no more than 30 minutes,no more than 15 minutes, no more than 10 minutes, no more than oneminute, no more than one second, and no more than every time interval inseconds between one second and one hour.

Some embodiments of the present tangible computer-readable media may be,for example, a CD-ROM, a DVD-ROM, a flash drive, a hard drive, or anyother physical storage device. Some embodiments of the present methodsmay include recording a tangible computer-readable medium withcomputer-readable code that, when executed by a computer, causes thecomputer to perform any of the operations discussed herein, includingthose associated with the present tangible computer-readable media.Recording the tangible computer-readable medium may include, forexample, burning data onto a CD-ROM or a DVD-ROM, or otherwisepopulating a physical storage device with the data. Expression data,diff pair values, scaling matrix values, and/or risk scores may bestored or processed according to embodiments discussed herein.

Other embodiments are discussed throughout this disclosure, such as inthe provided detailed description of the embodiments and the examples.Any embodiment discussed with respect to one aspect applies to otheraspects as well, and vice versa.

The terms “inhibiting,” “reducing,” or “preventing,” or any variation ofthese terms, when used in the claims and/or the specification includesany measurable decrease or complete inhibition to achieve a desiredresult.

The use of the word “a” or “an” when used in conjunction with the term“comprising” in the claims and/or the specification may mean “one,” butit is also consistent with the meaning of “one or more,” “at least one,”and “one or more than one.”

It is contemplated that any embodiment discussed herein can beimplemented with respect to any disclosed method or composition, andvice versa. Any embodiment discussed with respect to a particularpancreatic disorder can be applied or implemented with respect to adifferent pancreatic disorder. Furthermore, the disclosed compositionsand kits can be used to achieve the disclosed methods.

Throughout this application, the term “about” is used to indicate that avalue includes the standard deviation of error for the device or methodbeing employed to determine the value.

The use of the term “or” in the claims is used to mean “and/or” unlessexplicitly indicated to refer to alternatives only, or the alternativesare mutually exclusive.

As used in this specification and claim(s), the words “comprising” (andany form of comprising, such as “comprise” and “comprises”), “having”(and any form of having, such as “have” and “has”), “including” (and anyform of including, such as “includes” and “include”) or “containing”(and any form of containing, such as “contains” and “contain”) areinclusive or open-ended and do not exclude additional, unrecitedelements or method steps. However, for a claim using any of these terms,embodiments are also contemplated where the claim is closed and doesexclude additional, unrecited elements or method steps.

Other objects, features and advantages of the invention will be apparentfrom the following detailed description. It should be understood,however, that the detailed description and the specific examples, whileindicating specific embodiments, are given by way of illustration only,because various changes and modifications within the spirit and scope ofthe invention will be apparent to those skilled in the art from thisdetailed description.

DESCRIPTION OF THE DRAWINGS

The following drawings form part of the present specification and areincluded to further demonstrate certain aspects of the invention. Theinvention may be better understood by reference to one or more of thesedrawings in combination with the detailed description of specificembodiments presented herein.

FIG. 1. Assessing the ability of different models to distinguish betweenPDAC and CP samples using a training set of specimens. PDAC and CPformalin-fixed, paraffin-embedded (FFPE) samples were obtained and usedto evaluate different models for the ability to distinguish between PDACand CP samples.

FIG. 2. The LDA+ModT+6 model effectively distinguishes between PDAC andCP samples using a test set of specimens. PDAC and CP fine needleaspirate (FNA) samples were obtained and used to evaluate the LDA+ModT+6model for the ability to distinguish between PDAC and CP samples.

FIG. 3. Evaluation of the LDA+ModT+6 model as compared to a Simple Modelthat uses only one miRNA DiffPair to distinguish between PDAC and CPsamples.

FIG. 4. Post-hoc comparison of the LDA+ModT+6 model to the Reduced Modelin terms of ability to distinguish between PDAC and CP samples. TheReduced Model includes four miRNA DiffPairs: Diff(miR-135b,miR-24);Diff(miR-130b,miR-135b); Diff(miR-135b,miR-148a); andDiff(miR-148a,miR-196a).

FIG. 5. Assessing the ability of the LDA+ModT+6 Full Model, SimpleModel, and Reduced Model to correctly distinguish between PDAC and CP insamples that were determined to by cytologically atypical.

FIG. 6. This top table captures the performance of MP without adjustmentby a reflex test or additional QC procedures. The middle 2×2 table(confusion matrix) captures the raw results of calls by MP (Predicted)versus final diagnosis (Truth). Also included are overall accuracyderived from the 2×2 table and the area under the receiver operatingcharacteristic curve (AUC).

FIG. 7. The top diff pairs are shown sorted by the TriMean of thesimulated Wilcox-test FDR estimates (ExpectedFDR). The false discoveryrates (FDRs) are conservative because of the combinatorial nature of thepairings and subsequent hypothesis testing—the p-values are adjusted forall possible pairings. Results are based on 10 replications of 10-foldcross validation (100 total samplings). The replicated CV is balanced inthe sense that equal numbers of TN and FN are removed. The bottom boxshows basic statistics of the FDR estimates and how often the labeleddiff pair is the top ranked candidate in addition to the percentage ofsampled runs where the FDR is less than 10%.

FIG. 8. The plot shows the expected performance on the FNA sample setusing the Diff(miR-130b,miR-24) pair. The vertical lines capture 95% ofthe thresholds determined from replicated CV. The 2×2 tables show theperformance at the lower and upper thresholds. The lower threshold is4.08 while the upper threshold is 4.36. The data suggested here suggestsmaximum possible performance as we are training/testing on the samedata. The right set of 2×2 tables show the performance estimates at thetwo vertical dashed lines. Specifically, the top box shows the resultsat the lower threshold (left dashed line) and the bottom box shows theresults at the upper threshold (right dashed line). Both thresholds havevery similar estimates for overall accuracy, and the 95% confidenceintervals strongly overlap (data not shown). See main text for moreinformation.

FIG. 9. The figure captures classification results of MP stratified byDiff(miR-130b,miR-24) using the lower (left panel) and upper (rightpanel) thresholds. See main text for more information.

FIG. 10. The left panel shows the results of calls fromDiff(miR-130b,miR-24) outside of 95% of the thresholds tend to be verygood at classifying PDAC (increasing sensitivity) with an expected dropin accuracy classifying Benign (decreasing specificity). The calls inthis panel are consistent from all replications of cross-validation.Calls that were inconsistent on the reflex metric were set to incorrectpredictions. The right panel shows the results of applyingDiff(miR-130b,miR-24) to all 184 samples in the FNA validation study. Asexpected the overall accuracy increases because sensitivity increases.

FIG. 11. The figure shows the results of the FNA validation study withsamples PDAC by cytology excluded from analysis. Samples consistentlycalled PDAC by the reflex metric were set to PDAC, while samples withinconsistent reflex calls were set to opposite of Truth (classifiedincorrectly). Of particular importance is the sum of the lower bounds ofthe 95% CI for Sens+Spec and PPV+NPV are comparable across panels. Inother words, the reflex test essentially reflects a trade-off ofsensitivity and specificity.

FIG. 12. The figure shows the results of the FNA validation study withsamples PDAC and Suspicious by cytology excluded from analysis. Theassumptions and analysis presented here is comparable to that describedin FIG. 11 and the main text.

FIG. 13. The figure shows the results of the FNA validation study whereoutcome is determined by either Cytology or MP. Essentially, if eitherCytology or MP predicts a specimen as PDAC, the specimen is classifiedas PDAC. Otherwise, the sample is classified as Benign. The assumptionsand analysis presented here is comparable to that described in FIG. 11and the main text. Again, notice the sum of the lower bounds of the 95%CI for Sens+Spec and PPV+NPV. The reflex test again favors sensitivityat the cost specificity.

FIG. 14. The frequency plot shows how often a given diff pair isselected by the tree classifier based on the assumption of having only 3nodes in the tree (2 diff pair predictors). First, a full tree isgenerated, and then pruned to 3 nodes. The results are correlated withthe Wilcox test results (See FIG. 7). The feature selection is nestedwithin CV.

FIG. 15. The figure shows the top 2 diff pairs for the treeclassification results by frequency selection (See FIG. 14). Thehorizontal and vertical lines cover the threshold distribution for 95%of the iterations in the simulation for Diff(miR-130b,miR-24)(horizontal lines) and Diff(miR-155,miR-196a) (vertical lines). Samplesare shaped by classification status where circles are FNs and trianglesare TNs. The regions of uncertainty would be areas delimited by the 95%ranges.

FIG. 16. Final Diagnosis Drives Most of the Variance in the DiffSpace ofthe Merged Training and Test Sets. The figure shows principal componentanalysis (PCA) applied to the expression values of the 6 DiffPairscomposing MP stratified by sample type with the FFPE samples in the leftpanel and FNA samples in the right panel. The final diagnosis drivesmost of the 1st principal component (x-axis) while both final diagnosisand sample type drive most of the 2nd principal component (y-axis). Themarkers are shaped by final diagnosis with PDAC samples as circles andBenign samples as Xs.

FIG. 17. Biomarker expression values are mostly consistent betweensample types. Youden's Index (y-axis) summarizes the predictiveperformance of each DiffPair (biomarker) used in MP by panel. In orderfor the model to successfully migrate between sample types, the point ofmaximum performance (maximum Youden's Index) should be aligned betweenthe training set (FFPE—circles plus lines) and test set (FNA—trianglesplus lines). A deviation in alignment between curves would lead todifferences in performance estimates between sample types. Note that theperformance of Diff(miR-135b,miR-96) has the worst translatedperformance between sample types, but it was the 2nd least importantpredictor by weight in MP. The most important predictor,Diff(miR-135b,miR-24) was relatively well aligned between the trainingand test sets.

FIG. 18. Classification performance of MP stratified by Cytology.

FIG. 19. Overall diagnostic performance of FNA cytology alone (A) andthe MP test alone (B) for 184 FNA specimens using Final diagnosis as thegold standard.

FIG. 20. (A) Overall diagnostic performance of FNA cytology combinedwith the MP test for 184 FNA specimens using Final diagnosis as a goldstandard. In this context, a sample is predicted as PDAC if eithercytology or MP classifies a specimen as PDAC. Otherwise, the sample ispredicted as Benign. (B) Diagnostic performance of FNA cytology combinedwith the MP test on the 58 specimens in the benign and inconclusivecytology category (exclude PDAC by cytology) using Final diagnosis asgold standard.

DETAILED DESCRIPTION OF THE INVENTION

Certain embodiments are directed to compositions and methods relating topreparation and characterization of miRNAs, as well as use of miRNAs fortherapeutic, prognostic, and diagnostic applications, particularly thosemethods and compositions related to assessing and/or identifyingpancreatic disease.

I. miRNA MOLECULES

MicroRNA molecules (“miRNAs”) are generally 21 to 22 nucleotides inlength, though lengths of 19 and up to 23 nucleotides have beenreported. The miRNAs are each processed from a longer precursor RNAmolecule (“precursor miRNA”). Precursor miRNAs are transcribed fromnon-protein-encoding genes. The precursor miRNAs have two regions ofcomplementarity that enable them to form a stem-loop- or fold-back-likestructure, which is cleaved in animals by a ribonuclease III-likenuclease enzyme called Dicer. The processed miRNA is typically a portionof the stem.

The processed miRNA (also referred to as “mature miRNA”) becomes part ofa large complex to down-regulate a particular target gene. Examples ofanimal miRNAs include those that imperfectly basepair with the target,which halts translation of the target (Olsen et al., 1999; Seggerson etal., 2002). siRNA molecules also are processed by Dicer, but from along, double-stranded RNA molecule. siRNAs are not naturally found inanimal cells, but they can direct the sequence-specific cleavage of anmRNA target through an RNA-induced silencing complex (RISC) (Denli etal., 2003).

A. Nucleic Acids

In the disclosed compositions and methods miRNAs can be labeled, used inarray analysis, or employed in diagnostic, therapeutic, or prognosticapplications, particularly those related to pathological conditions ofthe pancreas. The RNA may have been endogenously produced by a cell, orbeen synthesized or produced chemically or recombinantly. They may beisolated and/or purified. The term “miRNA,” unless otherwise indicated,refers to the processed RNA, after it has been cleaved from itsprecursor. The name of the miRNA is often abbreviated and referred towithout a hsa-, mmu-, or rno-prefix and will be understood as such,depending on the context. Unless otherwise indicated, miRNAs referred toare human sequences identified as miR-X or let-X, where X is a numberand/or letter.

In certain experiments, a miRNA probe designated by a suffix “5P” or“3P” can be used. “5P” indicates that the mature miRNA derives from the5′ end of the precursor and a corresponding “3P” indicates that itderives from the 3′ end of the precursor, as described on the World WideWeb at sanger.ac.uk. Moreover, in some embodiments, a miRNA probe isused that does not correspond to a known human miRNA. It is contemplatedthat these non-human miRNA probes may be used in embodiments or thatthere may exist a human miRNA that is homologous to the non-human miRNA.While the methods and compositions are not limited to human miRNA, incertain embodiments, miRNA from human cells or a human biological sampleis used or evaluated. In other embodiments, any mammalian miRNA or cell,biological sample, or preparation thereof may be employed.

In some embodiments, methods and compositions involving miRNA mayconcern miRNA and/or other nucleic acids. Nucleic acids may be, be atleast, or be at most 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,105, 106, 107, 108, 109, 110, 120, 130, 140, 150, 160, 170, 180, 190,200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330,340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 441, 450, 460,470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 600,610, 620, 630, 640, 650, 660, 670, 680, 690, 700, 710, 720, 730, 740,750, 760, 770, 780, 790, 800, 810, 820, 830, 840, 850, 860, 870, 880,890, 900, 910, 920, 930, 940, 950, 960, 970, 980, 990, or 1000nucleotides, or any range derivable therein, in length. Such lengthscover the lengths of processed miRNA, miRNA probes, precursor miRNA,miRNA containing vectors, control nucleic acids, and other probes andprimers. In many embodiments, miRNAs are 19-24 nucleotides in length,while miRNA probes are 19-35 nucleotides in length, depending on thelength of the processed miRNA and any flanking regions added. miRNAprecursors are generally between 62 and 110 nucleotides in human s.

Nucleic acids used in methods and compositions disclosed herein may haveregions of identity or complementarity to another nucleic acid. It iscontemplated that the region of complementarity or identity can be atleast 5 contiguous residues, though it is specifically contemplated thatthe region is, is at least, or is at most 6, 7, 8, 9, 10, 11, 12, 13,14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49,50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 110, 120,130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260,270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400,410, 420, 430, 440, 441, 450, 460, 470, 480, 490, 500, 510, 520, 530,540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670,680, 690, 700, 710, 720, 730, 740, 750, 760, 770, 780, 790, 800, 810,820, 830, 840, 850, 860, 870, 880, 890, 900, 910, 920, 930, 940, 950,960, 970, 980, 990, or 1000, or any range derivable therein, contiguousnucleotides. It is further understood that the length of complementaritywithin a precursor miRNA or between a miRNA probe and a miRNA or a miRNAgene are such lengths. Moreover, the complementarity may be expressed asa percentage, meaning that the complementarity between a probe and itstarget is 90% or greater over the length of the probe. In someembodiments, complementarity is or is at least 90%, 95% or 100%. Inparticular, such lengths may be applied to any nucleic acid comprising anucleic acid sequence identified in any of the SEQ ID NOs disclosedherein. The commonly used name of the miRNA is given (with itsidentifying source in the prefix, for example, “hsa” for humansequences) and the processed miRNA sequence. Unless otherwise indicated,a miRNA without a prefix will be understood to refer to a human miRNA. AmiRNA designated, for example, as miR-1-2 in the application will beunderstood to refer to hsa-miR-1-2. Moreover, a lowercase letter in thename of a miRNA may or may not be lowercase; for example, hsa-mir-130bcan also be referred to as miR-130B. In addition, miRNA sequences with a“mu” or “mmu” sequence will be understood to refer to a mouse miRNA andmiRNA sequences with a “mo” sequence will be understood to refer to arat miRNA. The term “miRNA probe” refers to a nucleic acid probe thatcan identify a particular miRNA or structurally related miRNAs.

It is understood that a miRNA is derived from genomic sequences or agene. In this respect, the term “gene” is used for simplicity to referto the genomic sequence encoding the precursor miRNA for a given miRNA.However, embodiments may involve genomic sequences of a miRNA that areinvolved in its expression, such as a promoter or other regulatorysequences.

The term “recombinant” generally refers to a molecule that has beenmanipulated in vitro or that is a replicated or expressed product ofsuch a molecule.

The term “nucleic acid” is well known in the art. A “nucleic acid” asused herein will generally refer to a molecule (one or more strands) ofDNA, RNA or a derivative or analog thereof, comprising a nucleobase. Anucleobase includes, for example, a naturally occurring purine orpyrimidine base found in DNA (e.g., an adenine “A,” a guanine “G,” athymine “T” or a cytosine “C”) or RNA (e.g., an A, a G, an uracil “U” ora C). The term “nucleic acid” encompasses the terms “oligonucleotide”and “polynucleotide,” each as a subgenus of the term “nucleic acid.”

The term “miRNA” generally refers to a single-stranded molecule, but inspecific embodiments, molecules will also encompass a region or anadditional strand that is partially (between 10 and 50% complementaryacross length of strand), substantially (greater than 50% but less than100% complementary across length of strand) or fully complementary toanother region of the same single-stranded molecule or to anothernucleic acid. Thus, nucleic acids may encompass a molecule thatcomprises one or more complementary or self-complementary strand(s) or“complement(s)” of a particular sequence comprising a molecule. Forexample, precursor miRNA may have a self-complementary region, which isup to 100% complementary. miRNA probes or nucleic acids can include, canbe, or can be at least 60, 65, 70, 75, 80, 85, 90, 95, 96, 97, 98, 99 or100% complementary to their target.

As used herein, “hybridization”, “hybridizes” or “capable ofhybridizing” is understood to mean the forming of a double or triplestranded molecule or a molecule with partial double or triple strandednature. The term “anneal” is synonymous with “hybridize.” The term“hybridization”, “hybridize(s)” or “capable of hybridizing” encompassesthe terms “stringent condition(s)” or “high stringency” and the terms“low stringency” or “low stringency condition(s).”

As used herein, “stringent condition(s)” or “high stringency” are thoseconditions that allow hybridization between or within one or morenucleic acid strand(s) containing complementary sequence(s), butpreclude hybridization of random sequences. Stringent conditionstolerate little, if any, mismatch between a nucleic acid and a targetstrand. Such conditions are well known to those of ordinary skill in theart, and are preferred for applications requiring high selectivity.Non-limiting applications include isolating a nucleic acid, such as agene or a nucleic acid segment thereof, or detecting at least onespecific mRNA transcript or a nucleic acid segment thereof, and thelike.

Stringent conditions may comprise low salt and/or high temperatureconditions, such as provided by about 0.02 M to about 0.5 M NaCl attemperatures of about 42° C. to about 70° C. It is understood that thetemperature and ionic strength of a desired stringency are determined inpart by the length of the particular nucleic acid(s), the length andnucleobase content of the target sequence(s), the charge composition ofthe nucleic acid(s), and to the presence or concentration of formamide,tetramethylammonium chloride or other solvent(s) in a hybridizationmixture.

It is also understood that these ranges, compositions and conditions forhybridization are mentioned by way of non-limiting examples only, andthat the desired stringency for a particular hybridization reaction isoften determined empirically by comparison to one or more positive ornegative controls. Depending on the application envisioned it ispreferred to employ varying conditions of hybridization to achievevarying degrees of selectivity of a nucleic acid towards a targetsequence. In a non-limiting example, identification or isolation of arelated target nucleic acid that does not hybridize to a nucleic acidunder stringent conditions may be achieved by hybridization at lowtemperature and/or high ionic strength. Such conditions are termed “lowstringency” or “low stringency conditions,” and non-limiting examples ofsuch include hybridization performed at about 0.15 M to about 0.9 M NaClat a temperature range of about 20° C. to about 50° C. Of course, it iswithin the skill of one in the art to further modify the low or highstringency conditions to suite a particular application.

1. Nucleobases

As used herein a “nucleobase” refers to a heterocyclic base, such as forexample a naturally occurring nucleobase (i.e., an A, T, G, C or U)found in at least one naturally occurring nucleic acid (i.e., DNA andRNA), and naturally or non-naturally occurring derivative(s) and analogsof such a nucleobase. A nucleobase generally can form one or morehydrogen bonds (“anneal” or “hybridize”) with at least one naturallyoccurring nucleobase in a manner that may substitute for naturallyoccurring nucleobase pairing (e.g., the hydrogen bonding between A andT, G and C, and A and U).

“Purine” and/or “pyrimidine” nucleobase(s) encompass naturally occurringpurine and/or pyrimidine nucleobases and also derivative(s) andanalog(s) thereof, including but not limited to, those with a purine orpyrimidine substituted by one or more of an alkyl, caboxyalkyl, amino,hydroxyl, halogen (i.e., fluoro, chloro, bromo, or iodo), thiol oralkylthiol moiety. Preferred alkyl (e.g., alkyl, caboxyalkyl, etc.)moieties comprise of from about 1, about 2, about 3, about 4, about 5,to about 6 carbon atoms. Other non-limiting examples of a purine orpyrimidine include a deazapurine, a 2,6-diaminopurine, a 5-fluorouracil,a xanthine, a hypoxanthine, a 8-bromoguanine, a 8-chloroguanine, abromothymine, a 8-aminoguanine, a 8-hydroxyguanine, a 8-methylguanine, a8-thioguanine, an azaguanine, a 2-aminopurine, a 5-ethylcytosine, a5-methylcyosine, a 5-bromouracil, a 5-ethyluracil, a 5-iodouracil, a5-chlorouracil, a 5-propyluracil, a thiouracil, a 2-methyladenine, amethylthioadenine, a N,N-diemethyladenine, an azaadenines, a8-bromoadenine, a 8-hydroxyadenine, a 6-hydroxyaminopurine, a6-thiopurine, a 4-(6-aminohexyl/cytosine), and the like. Other examplesare well known to those of skill in the art.

A nucleobase may be comprised in a nucleoside or nucleotide, using anychemical or natural synthesis method described herein or known to one ofordinary skill in the art. Such a nucleobase may be labeled or may bepart of a molecule that is labeled and contains the nucleobase.

2. Nucleosides

As used herein, a “nucleoside” refers to an individual chemical unitcomprising a nucleobase covalently attached to a nucleobase linkermoiety. A non-limiting example of a “nucleobase linker moiety” is asugar comprising 5-carbon atoms (i.e., a “5-carbon sugar”), includingbut not limited to a deoxyribose, a ribose, an arabinose, or aderivative or an analog of a 5-carbon sugar. Non-limiting examples of aderivative or an analog of a 5-carbon sugar include a2′-fluoro-2′-deoxyribose or a carbocyclic sugar where a carbon issubstituted for an oxygen atom in the sugar ring.

Different types of covalent attachment(s) of a nucleobase to anucleobase linker moiety are known in the art. By way of non-limitingexample, a nucleoside comprising a purine (i.e., A or G) or a7-deazapurine nucleobase typically covalently attaches the 9 position ofa purine or a 7-deazapurine to the 1′-position of a 5-carbon sugar. Inanother non-limiting example, a nucleoside comprising a pyrimidinenucleobase (i.e., C, T or U) typically covalently attaches a 1 positionof a pyrimidine to a 1′-position of a 5-carbon sugar (Kornberg andBaker, 1992).

3. Nucleotides

As used herein, a “nucleotide” refers to a nucleoside further comprisinga “backbone moiety”. A backbone moiety generally covalently attaches anucleotide to another molecule comprising a nucleotide, or to anothernucleotide to form a nucleic acid. The “backbone moiety” in naturallyoccurring nucleotides typically comprises a phosphorus moiety, which iscovalently attached to a 5-carbon sugar. The attachment of the backbonemoiety typically occurs at either the 3′- or 5′-position of the 5-carbonsugar. However, other types of attachments are known in the art,particularly when a nucleotide comprises derivatives or analogs of anaturally occurring 5-carbon sugar or phosphorus moiety.

4. Nucleic Acid Analogs

A nucleic acid may comprise, or be composed entirely of, a derivative oranalog of a nucleobase, a nucleobase linker moiety and/or backbonemoiety that may be present in a naturally occurring nucleic acid. RNAwith nucleic acid analogs may also be labeled according to methodsdisclosed herein. As used herein a “derivative” refers to a chemicallymodified or altered form of a naturally occurring molecule, while theterms “mimic” or “analog” refer to a molecule that may or may notstructurally resemble a naturally occurring molecule or moiety, butpossesses similar functions. As used herein, a “moiety” generally refersto a smaller chemical or molecular component of a larger chemical ormolecular structure. Nucleobase, nucleoside, and nucleotide analogs orderivatives are well known in the art, and have been described (see forexample, Scheit, 1980, incorporated herein by reference).

Additional non-limiting examples of nucleosides, nucleotides, or nucleicacids comprising 5-carbon sugar and/or backbone moiety derivatives oranalogs, include those in: U.S. Pat. No. 5,681,947, which describesoligonucleotides comprising purine derivatives that form triple helixeswith and/or prevent expression of dsDNA; U.S. Pat. Nos. 5,652,099 and5,763,167, which describe nucleic acids incorporating fluorescentanalogs of nucleosides found in DNA or RNA, particularly for use asfluorescent nucleic acid probes; U.S. Pat. No. 5,614,617, whichdescribes oligonucleotide analogs with substitutions on pyrimidine ringsthat possess enhanced nuclease stability; U.S. Pat. Nos. 5,670,663,5,872,232 and 5,859,221, which describe oligonucleotide analogs withmodified 5-carbon sugars (i.e., modified 2′-deoxyfuranosyl moieties)used in nucleic acid detection; U.S. Pat. No. 5,446,137, which describesoligonucleotides comprising at least one 5-carbon sugar moietysubstituted at the 4′ position with a substituent other than hydrogenthat can be used in hybridization assays; U.S. Pat. No. 5,886,165, whichdescribes oligonucleotides with both deoxyribonucleotides with 3′-5′internucleotide linkages and ribonucleotides with 2′-5′ internucleotidelinkages; U.S. Pat. No. 5,714,606, which describes a modifiedinternucleotide linkage wherein a 3′-position oxygen of theinternucleotide linkage is replaced by a carbon to enhance the nucleaseresistance of nucleic acids; U.S. Pat. No. 5,672,697, which describesoligonucleotides containing one or more 5′ methylene phosphonateinternucleotide linkages that enhance nuclease resistance; U.S. Pat.Nos. 5,466,786 and 5,792,847, which describe the linkage of asubstituent moiety which may comprise a drug or label to the 2′ carbonof an oligonucleotide to provide enhanced nuclease stability and abilityto deliver drugs or detection moieties; U.S. Pat. No. 5,223,618, whichdescribes oligonucleotide analogs with a 2 or 3 carbon backbone linkageattaching the 4′ position and 3′ position of adjacent 5-carbon sugarmoiety to enhanced cellular uptake, resistance to nucleases andhybridization to target RNA; U.S. Pat. No. 5,470,967, which describesoligonucleotides comprising at least one sulfamate or sulfamideinternucleotide linkage that are useful as nucleic acid hybridizationprobe; U.S. Pat. Nos. 5,378,825, 5,777,092, 5,623,070, 5,610,289 and5,602,240, which describe oligonucleotides with three or four atomlinker moiety replacing phosphodiester backbone moiety used for improvednuclease resistance, cellular uptake, and regulating RNA expression;U.S. Pat. No. 5,858,988, which describes hydrophobic carrier agentattached to the 2′-O position of oligonucleotides to enhanced theirmembrane permeability and stability; U.S. Pat. No. 5,214,136, whichdescribes oligonucleotides conjugated to anthraquinone at the 5′terminus that possess enhanced hybridization to DNA or RNA; enhancedstability to nucleases; U.S. Pat. No. 5,700,922, which describesPNA-DNA-PNA chimeras wherein the DNA comprises2′-deoxy-erythro-pentofuranosyl nucleotides for enhanced nucleaseresistance, binding affinity, and ability to activate RNase H; and U.S.Pat. No. 5,708,154, which describes RNA linked to a DNA to form aDNA-RNA hybrid; U.S. Pat. No. 5,728,525, which describes the labeling ofnucleoside analogs with a universal fluorescent label.

Additional teachings for nucleoside analogs and nucleic acid analogs areU.S. Pat. No. 5,728,525, which describes nucleoside analogs that areend-labeled; U.S. Pat. Nos. 5,637,683, 6,251,666 (L-nucleotidesubstitutions), and 5,480,980 (7-deaza-2′ deoxyguanosine nucleotides andnucleic acid analogs thereof).

5. Modified Nucleotides

Labeling methods and kits may use nucleotides that are both modified forattachment of a label and can be incorporated into a miRNA molecule.Such nucleotides include those that can be labeled with a dye, includinga fluorescent dye, or with a molecule such as biotin. Labelednucleotides are readily available; they can be acquired commercially orthey can be synthesized by reactions known to those of skill in the art.

Modified nucleotides for use in the methods and compositions are notnaturally occurring nucleotides, but instead, refer to preparednucleotides that have a reactive moiety on them. Specific reactivefunctionalities of interest include: amino, sulfhydryl, sulfoxyl,aminosulfhydryl, azido, epoxide, isothiocyanate, isocyanate, anhydride,monochlorotriazine, dichlorotriazine, mono- or dihalogen substitutedpyridine, mono- or disubstituted diazine, maleimide, epoxide, aziridine,sulfonyl halide, acid halide, alkyl halide, aryl halide, alkylsulfonate,N-hydroxysuccinimide ester, imido ester, hydrazine, azidonitrophenyl,azide, 3-(2-pyridyl dithio)-propionamide, glyoxal, aldehyde, iodoacetyl,cyanomethyl ester, p-nitrophenyl ester, o-nitrophenyl ester,hydroxypyridine ester, carbonyl imidazole, and other such chemicalgroups. In some embodiments, the reactive functionality may be bondeddirectly to a nucleotide, or it may be bonded to the nucleotide througha linking group. The functional moiety and any linker cannotsubstantially impair the ability of the nucleotide to be added to themiRNA or to be labeled. Representative linking groups include carboncontaining linking groups, typically ranging from about 2 to 18, usuallyfrom about 2 to 8 carbon atoms, where the carbon containing linkinggroups may or may not include one or more heteroatoms, e.g. S, O, Netc., and may or may not include one or more sites of unsaturation. Ofparticular interest in some embodiments are alkyl linking groups,typically lower alkyl linking groups of 1 to 16, usually 1 to 4 carbonatoms, where the linking groups may include one or more sites ofunsaturation. The functionalized nucleotides (or primers) used in theabove methods of functionalized target generation may be fabricatedusing known protocols or purchased from commercial vendors, e.g., Sigma,Roche, Ambion, etc. Functional groups may be prepared according to waysknown to those of skill in the art, including the representativeinformation found in U.S. Pat. Nos. 4,404,289; 4,405,711; 4,337,063 and5,268,486, and U.K. Patent 1,529,202, which are all incorporated byreference.

Amine-modified nucleotides are used in some embodiments. Theamine-modified nucleotide is a nucleotide that has a reactive aminegroup for attachment of the label. It is contemplated that anyribonucleotide (G, A, U, or C) or deoxyribonucleotide (G, A, T, or C)can be modified for labeling. Examples include, but are not limited to,the following modified ribo- and deoxyribo-nucleotides:5-(3-aminoallyl)-UTP; 8-[(4-amino)butyl]-amino-ATP and8-[(6-amino)butyl]-amino-ATP; N6-(4-amino)butyl-ATP,N6-(6-amino)butyl-ATP, N4-[2,2-oxy-bis-(ethylamine)]-CTP;N6-(6-Amino)hexyl-ATP; 8-[(6-Amino)hexyl]-amino-ATP;5-propargylamino-CTP, 5-propargylamino-UTP; 5-(3-aminoallyl)-dUTP;8-[(4-amino)butyl]-amino-dATP and 8-[(6-amino)butyl]-amino-dATP;N6-(4-amino)butyl-dATP, N6-(6-amino)butyl-dATP,N4-[2,2-oxy-bis-(ethylamine)]-dCTP; N6-(6-Amino)hexyl-dATP;8-[(6-Amino)hexyl]-amino-dATP; 5-propargylamino-dCTP, and5-propargylamino-dUTP. Such nucleotides can be prepared according tomethods known to those of skill in the art. Moreover, a person ofordinary skill in the art could prepare other nucleotide entities withthe same amine-modification, such as a 5-(3-aminoallyl)-CTP, GTP, ATP,dCTP, dGTP, dTTP, or dUTP in place of a 5-(3-aminoallyl)-UTP.

B. Preparation of Nucleic Acids

A nucleic acid may be made by any technique known to one of ordinaryskill in the art, such as for example, chemical synthesis, enzymaticproduction, or biological production. It is specifically contemplatedthat miRNA probes are chemically synthesized.

In some embodiments, miRNAs are recovered or isolated from a biologicalsample. The miRNA may be recombinant or it may be natural or endogenousto the cell (produced from the cell's genome). It is contemplated that abiological sample may be treated in a way so as to enhance the recoveryof small RNA molecules such as miRNA. U.S. patent application Ser. No.10/667,126 describes such methods and is specifically incorporatedherein by reference. Generally, methods involve lysing cells with asolution having guanidinium and a detergent.

Alternatively, nucleic acid synthesis is performed according to standardmethods. See, for example, Itakura and Riggs (1980). Additionally, U.S.Pat. Nos. 4,704,362, 5,221,619, and 5,583,013 each describe variousmethods of preparing synthetic nucleic acids. Non-limiting examples of asynthetic nucleic acid (e.g., a synthetic oligonucleotide) include anucleic acid made by in vitro chemical synthesis using phosphotriester,phosphite, or phosphoramidite chemistry and solid phase techniques suchas described in EP 266,032, incorporated herein by reference, or viadeoxynucleoside H-phosphonate intermediates as described by Froehler etal., 1986 and U.S. Pat. No. 5,705,629, each incorporated herein byreference. In some methods, one or more oligonucleotide may be used.Various different mechanisms of oligonucleotide synthesis have beendisclosed in for example, U.S. Pat. Nos. 4,659,774, 4,816,571,5,141,813, 5,264,566, 4,959,463, 5,428,148, 5,554,744, 5,574,146,5,602,244, each of which is incorporated herein by reference.

A non-limiting example of an enzymatically produced nucleic acid includeone produced by enzymes in amplification reactions such as PCR™ (see forexample, U.S. Pat. Nos. 4,683,202 and 4,682,195, each incorporatedherein by reference), or the synthesis of an oligonucleotide asdescribed in U.S. Pat. No. 5,645,897, incorporated herein by reference.A non-limiting example of a biologically produced nucleic acid includesa recombinant nucleic acid produced (i.e., replicated) in a living cell,such as a recombinant DNA vector replicated in bacteria (see forexample, Sambrook et al., 2001, incorporated herein by reference).

Oligonucleotide synthesis is well known to those of skill in the art.Various different mechanisms of oligonucleotide synthesis have beendisclosed in for example, U.S. Pat. Nos. 4,659,774, 4,816,571,5,141,813, 5,264,566, 4,959,463, 5,428,148, 5,554,744, 5,574,146,5,602,244, each of which is incorporated herein by reference.

Basically, chemical synthesis can be achieved by the diester method, thetriester method, polynucleotide phosphorylase method, and by solid-phasechemistry. The diester method was the first to be developed to a usablestate, primarily by Khorana and co-workers. (Khorana, 1979). The basicstep is the joining of two suitably protected deoxynucleotides to form adideoxynucleotide containing a phosphodiester bond.

The main difference between the diester and triester methods is thepresence in the latter of an extra protecting group on the phosphateatoms of the reactants and products (Itakura et al., 1975).Purifications are typically done in chloroform solutions. Otherimprovements in the method include (i) the block coupling of trimers andlarger oligomers, (ii) the extensive use of high-performance liquidchromatography for the purification of both intermediate and finalproducts, and (iii) solid-phase synthesis.

Polynucleotide phosphorylase method is an enzymatic method of DNAsynthesis that can be used to synthesize many useful oligonucleotides(Gillam et al., 1978; Gillam et al., 1979). Under controlled conditions,polynucleotide phosphorylase adds predominantly a single nucleotide to ashort oligonucleotide. Chromatographic purification allows the desiredsingle adduct to be obtained. At least a trimer is required to start theprocedure, and this primer must be obtained by some other method. Thepolynucleotide phosphorylase method works and has the advantage that theprocedures involved are familiar to most biochemists.

Solid-phase methods draw on technology developed for the solid-phasesynthesis of polypeptides. It has been possible to attach the initialnucleotide to solid support material and proceed with the stepwiseaddition of nucleotides. All mixing and washing steps are simplified,and the procedure becomes amenable to automation. These syntheses arenow routinely carried out using automatic nucleic acid synthesizers.

Phosphoramidite chemistry (Beaucage and Lyer, 1992) has become the mostwidely used coupling chemistry for the synthesis of oligonucleotides.Phosphoramidite synthesis of oligonucleotides involves activation ofnucleoside phosphoramidite monomer precursors by reaction with anactivating agent to form activated intermediates, followed by sequentialaddition of the activated intermediates to the growing oligonucleotidechain (generally anchored at one end to a suitable solid support) toform the oligonucleotide product.

Recombinant methods for producing nucleic acids in a cell are well knownto those of skill in the art. These include the use of vectors (viraland non-viral), plasmids, cosmids, and other vehicles for delivering anucleic acid to a cell, which may be the target cell (e.g., a cancercell) or simply a host cell (to produce large quantities of the desiredRNA molecule). Alternatively, such vehicles can be used in the contextof a cell free system so long as the reagents for generating the RNAmolecule are present. Such methods include those described in Sambrook,2003, Sambrook, 2001 and Sambrook, 1989, which are hereby incorporatedby reference.

In certain embodiments, nucleic acid molecules are not synthetic. Insome embodiments, the nucleic acid molecule has a chemical structure ofa naturally occurring nucleic acid and a sequence of a naturallyoccurring nucleic acid, such as the exact and entire sequence of asingle stranded primary miRNA (see Lee 2002), a single-strandedprecursor miRNA, or a single-stranded mature miRNA. In addition to theuse of recombinant technology, such non-synthetic nucleic acids may begenerated chemically, such as by employing technology used for creatingoligonucleotides.

C. Isolation of Nucleic Acids

Nucleic acids may be isolated using techniques well known to those ofskill in the art, though in particular embodiments, methods forisolating small nucleic acid molecules, and/or isolating RNA moleculescan be employed. Chromatography is a process often used to separate orisolate nucleic acids from protein or from other nucleic acids. Suchmethods can involve electrophoresis with a gel matrix, filter columns,alcohol precipitation, and/or other chromatography. If miRNA from cellsis to be used or evaluated, methods generally involve lysing the cellswith a chaotropic (e.g., guanidinium isothiocyanate) and/or detergent(e.g., N-lauroyl sarcosine) prior to implementing processes forisolating particular populations of RNA.

In particular methods for separating miRNA from other nucleic acids, agel matrix is prepared using polyacrylamide, though agarose can also beused. The gels may be graded by concentration or they may be uniform.Plates or tubing can be used to hold the gel matrix for electrophoresis.Usually one-dimensional electrophoresis is employed for the separationof nucleic acids. Plates are used to prepare a slab gel, while thetubing (glass or rubber, typically) can be used to prepare a tube gel.The phrase “tube electrophoresis” refers to the use of a tube or tubing,instead of plates, to form the gel. Materials for implementing tubeelectrophoresis can be readily prepared by a person of skill in the artor purchased.

Methods may involve the use of organic solvents and/or alcohol toisolate nucleic acids, particularly miRNA used in methods andcompositions disclosed herein. Some embodiments are described in U.S.patent application Ser. No. 10/667,126, which is hereby incorporated byreference. Generally, this disclosure provides methods for efficientlyisolating small RNA molecules from cells comprising: adding an alcoholsolution to a cell lysate and applying the alcohol/lysate mixture to asolid support before eluting the RNA molecules from the solid support.In some embodiments, the amount of alcohol added to a cell lysateachieves an alcohol concentration of about 55% to 60%. While differentalcohols can be employed, ethanol works well. A solid support may be anystructure, and it includes beads, filters, and columns, which mayinclude a mineral or polymer support with electronegative groups. Aglass fiber filter or column may work particularly well for suchisolation procedures.

In specific embodiments, miRNA isolation processes include: a) lysingcells in the sample with a lysing solution comprising guanidinium,wherein a lysate with a concentration of at least about 1 M guanidiniumis produced; b) extracting miRNA molecules from the lysate with anextraction solution comprising phenol; c) adding to the lysate analcohol solution for forming a lysate/alcohol mixture, wherein theconcentration of alcohol in the mixture is between about 35% to about70%; d) applying the lysate/alcohol mixture to a solid support; e)eluting the miRNA molecules from the solid support with an ionicsolution; and, f) capturing the miRNA molecules. Typically the sample isdried down and resuspended in a liquid and volume appropriate forsubsequent manipulation.

II. LABELS AND LABELING TECHNIQUES

In some embodiments, miRNAs are labeled. It is contemplated that miRNAmay first be isolated and/or purified prior to labeling. This mayachieve a reaction that more efficiently labels the miRNA, as opposed toother RNA in a sample in which the miRNA is not isolated or purifiedprior to labeling. In particular embodiments, the label isnon-radioactive. Generally, nucleic acids may be labeled by addinglabeled nucleotides (one-step process) or adding nucleotides andlabeling the added nucleotides (two-step process).

A. Labeling Techniques

In some embodiments, nucleic acids are labeled by catalytically addingto the nucleic acid an already labeled nucleotide or nucleotides. One ormore labeled nucleotides can be added to miRNA molecules. See U.S. Pat.No. 6,723,509, which is hereby incorporated by reference.

In other embodiments, an unlabeled nucleotide(s) is catalytically addedto a miRNA, and the unlabeled nucleotide is modified with a chemicalmoiety that enables it to be subsequently labeled. In some embodiments,the chemical moiety is a reactive amine such that the nucleotide is anamine-modified nucleotide. Examples of amine-modified nucleotides arewell known to those of skill in the art, many being commerciallyavailable.

In contrast to labeling of cDNA during its synthesis, the issue forlabeling miRNA is how to label the already existing molecule. Someaspects concern the use of an enzyme capable of using a di- ortri-phosphate ribonucleotide or deoxyribonucleotide as a substrate forits addition to a miRNA. Moreover, in specific embodiments, a modifieddi- or tri-phosphate ribonucleotide is added to the 3′ end of a miRNA.The source of the enzyme is not limiting. Examples of sources for theenzymes include yeast, gram-negative bacteria such as E. coli,lactococcus lactis, and sheep pox virus.

Enzymes capable of adding such nucleotides include, but are not limitedto, poly(A) polymerase, terminal transferase, and polynucleotidephosphorylase. In specific embodiments, a ligase is contemplated as notbeing the enzyme used to add the label, and instead, a non-ligase enzymeis employed.

Terminal transferase may catalyze the addition of nucleotides to the 3′terminus of a nucleic acid. Polynucleotide phosphorylase can polymerizenucleotide diphosphates without the need for a primer.

B. Labels

Labels on miRNA or miRNA probes may be colorimetric (includes visibleand UV spectrum, including fluorescent), luminescent, enzymatic, orpositron emitting (including radioactive). The label may be detecteddirectly or indirectly. Radioactive labels include ¹²⁵I, ³²P, ³³P, and³⁵S. Examples of enzymatic labels include alkaline phosphatase,luciferase, horseradish peroxidase, and β-galactosidase. Labels can alsobe proteins with luminescent properties, e.g., green fluorescent proteinand phicoerythrin.

The colorimetric and fluorescent labels contemplated for use asconjugates include, but are not limited to, Alexa Fluor dyes, BODIPYdyes, such as BODIPY FL; Cascade Blue; Cascade Yellow; coumarin and itsderivatives, such as 7-amino-4-methylcoumarin, aminocoumarin andhydroxycoumarin; cyanine dyes, such as Cy3 and Cy5; eosins anderythrosins; fluorescein and its derivatives, such as fluoresceinisothiocyanate; macrocyclic chelates of lanthanide ions, such as QuantumDye™; Marina Blue; Oregon Green; rhodamine dyes, such as rhodamine red,tetramethylrhodamine and rhodamine 6G; Texas Red; fluorescent energytransfer dyes, such as thiazole orange-ethidium heterodimer; and, TOTAB.

Specific examples of dyes include, but are not limited to, thoseidentified above and the following: Alexa Fluor 350, Alexa Fluor 405,Alexa Fluor 430, Alexa Fluor 488, Alexa Fluor 500. Alexa Fluor 514,Alexa Fluor 532, Alexa Fluor 546, Alexa Fluor 555, Alexa Fluor 568,Alexa Fluor 594, Alexa Fluor 610, Alexa Fluor 633, Alexa Fluor 647,Alexa Fluor 660, Alexa Fluor 680, Alexa Fluor 700, and, Alexa Fluor 750;amine-reactive BODIPY dyes, such as BODIPY 493/503, BODIPY 530/550,BODIPY 558/568, BODIPY 564/570, BODIPY 576/589, BODIPY 581/591, BODIPY630/650, BODIPY 650/655, BODIPY FL, BODIPY R6G, BODIPY TMR, and,BODIPY-TR; Cy3, Cy5,6-FAM, Fluorescein Isothiocyanate, HEX, 6-JOE,Oregon Green 488, Oregon Green 500, Oregon Green 514, Pacific Blue, REG,Rhodamine Green, Rhodamine Red, Renographin, ROX, SYPRO, TAMRA,2′,4′,5′7′-Tetrabromosulfonefluorescein, and TET.

Specific examples of fluorescently labeled ribonucleotides include AlexaFluor 488-5-UTP, Fluorescein-12-UTP, BODIPY FL-14-UTP, BODIPYTMR-14-UTP, Tetramethylrhodamine-6-UTP, Alexa Fluor 546-14-UTP, TexasRed-5-UTP, and BODIPY TR-14-UTP. Other fluorescent ribonucleotidesinclude Cy3-UTP and Cy5-UTP.

Examples of fluorescently labeled deoxyribonucleotides includeDinitrophenyl (DNP)-11-dUTP, Cascade Blue-7-dUTP, Alexa Fluor488-5-dUTP, Fluorescein-12-dUTP, Oregon Green 488-5-dUTP, BODIPYFL-14-dUTP, Rhodamine Green-5-dUTP, Alexa Fluor 532-5-dUTP, BODIPYTMR-14-dUTP, Tetramethylrhodamine-6-dUTP, Alexa Fluor 546-14-dUTP, AlexaFluor 568-5-dUTP, Texas Red-12-dUTP, Texas Red-5-dUTP, BODIPYTR-14-dUTP, Alexa Fluor 594-5-dUTP, BODIPY 630/650-14-dUTP, BODIPY650/665-14-dUTP; Alexa Fluor 488-7-OBEA-dCTP, Alexa Fluor546-16-OBEA-dCTP, Alexa Fluor 594-7-OBEA-dCTP, and Alexa Fluor647-12-OBEA-dCTP.

It is contemplated that nucleic acids may be labeled with two differentlabels. Furthermore, fluorescence resonance energy transfer (FRET) maybe employed in disclosed methods (e.g., Klostermeier et al., 2002;Emptage, 2001; Didenko, 2001, each incorporated by reference).

Alternatively, the label may not be detectable per se, but indirectlydetectable or allowing for the isolation or separation of the targetednucleic acid. For example, the label could be biotin, digoxigenin,polyvalent cations, chelator groups and other ligands, include ligandsfor an antibody.

C. Visualization Techniques

A number of techniques for visualizing or detecting labeled nucleicacids are readily available. Such techniques include, microscopy,arrays, fluorometry, light cyclers or other real time PCR machines, FACSanalysis, scintillation counters, phosphoimagers, Geiger counters, MRI,CAT, antibody-based detection methods (Westerns, immunofluorescence,immunohistochemistry), histochemical techniques, HPLC (Griffey et al.,1997), spectroscopy, capillary gel electrophoresis (Cummins et al.,1996), spectroscopy; mass spectroscopy; radiological techniques; andmass balance techniques.

When two or more differentially colored labels are employed, fluorescentresonance energy transfer (FRET) techniques may be employed tocharacterize association of one or more nucleic acids. Furthermore, aperson of ordinary skill in the art is well aware of ways ofvisualizing, identifying, and characterizing labeled nucleic acids, andaccordingly, such protocols may be used. Examples of tools that may beused also include fluorescent microscopy, a BioAnalyzer, a plate reader,Storm (Molecular Dynamics), Array Scanner, FACS (fluorescent activatedcell sorter), or any instrument that has the ability to excite anddetect a fluorescent molecule.

III. ARRAY PREPARATION AND SCREENING A. Array Preparation

Some embodiments involve the preparation and use of miRNA arrays ormiRNA probe arrays, which are ordered macroarrays or microarrays ofnucleic acid molecules (probes) that are fully or nearly complementaryor identical to a plurality of miRNA molecules or precursor miRNAmolecules and that are positioned on a support or support material in aspatially separated organization. Macroarrays are typically sheets ofnitrocellulose or nylon upon which probes have been spotted. Microarraysposition the nucleic acid probes more densely such that up to 10,000nucleic acid molecules can be fit into a region typically 1 to 4 squarecentimeters. Microarrays can be fabricated by spotting nucleic acidmolecules, e.g., genes, oligonucleotides, etc., onto substrates orfabricating oligonucleotide sequences in situ on a substrate. Spotted orfabricated nucleic acid molecules can be applied in a high densitymatrix pattern of up to about 30 non-identical nucleic acid moleculesper square centimeter or higher, e.g. up to about 100 or even 1000 persquare centimeter. Microarrays typically use coated glass as the solidsupport, in contrast to the nitrocellulose-based material of filterarrays. By having an ordered array of miRNA-complementing nucleic acidsamples, the position of each sample can be tracked and linked to theoriginal sample. A variety of different array devices in which aplurality of distinct nucleic acid probes are stably associated with thesurface of a solid support are known to those of skill in the art.Useful substrates for arrays include nylon, glass, metal, plastic, andsilicon. Such arrays may vary in a number of different ways, includingaverage probe length, sequence or types of probes, nature of bondbetween the probe and the array surface, e.g. covalent or non-covalent,and the like. The labeling and screening methods are not limited by withrespect to any parameter except that the probes detect miRNA;consequently, methods and compositions may be used with a variety ofdifferent types of miRNA arrays.

Representative methods and apparatuses for preparing a microarray havebeen described, for example, in U.S. Pat. Nos. 5,143,854; 5,202,231;5,242,974; 5,288,644; 5,324,633; 5,384,261; 5,405,783; 5,412,087;5,424,186; 5,429,807; 5,432,049; 5,436,327; 5,445,934; 5,468,613;5,470,710; 5,472,672; 5,492,806; 5,525,464; 5,503,980; 5,510,270;5,525,464; 5,527,681; 5,529,756; 5,532,128; 5,545,531; 5,547,839;5,554,501; 5,556,752; 5,561,071; 5,571,639; 5,580,726; 5,580,732;5,593,839; 5,599,695; 5,599,672; 5,610,287; 5,624,711; 5,631,134;5,639,603; 5,654,413; 5,658,734; 5,661,028; 5,665,547; 5,667,972;5,695,940; 5,700,637; 5,744,305; 5,800,992; 5,807,522; 5,830,645;5,837,196; 5,871,928; 5,847,219; 5,876,932; 5,919,626; 6,004,755;6,087,102; 6,368,799; 6,383,749; 6,617,112; 6,638,717; 6,720,138, aswell as WO 93/17126; WO 95/11995; WO 95/21265; WO 95/21944; WO 95/35505;WO 96/31622; WO 97/10365; WO 97/27317; WO 99/35505; WO 09923256; WO09936760; WO0138580; WO 0168255; WO 03020898; WO 03040410; WO 03053586;WO 03087297; WO 03091426; WO03100012; WO 04020085; WO 04027093; EP 373203; EP 785 280; EP 799 897 and UK 8 803 000, which are each hereinincorporated by reference.

It is contemplated that the arrays can be high density arrays, such thatthey contain 2, 20, 25, 50, 80, 100, or more, or any integer derivabletherein, different probes. It is contemplated that they may contain1000, 16,000, 65,000, 250,000 or 1,000,000 or more, or any interger orrange derivable therein, different probes. The probes can be directed totargets in one or more different organisms or cell types. In someembodiments, the oligonucleotide probes may range from 5 to 50, 5 to 45,10 to 40, 9 to 34, or 15 to 40 nucleotides in length. In certainembodiments, the oligonucleotide probes are 5, 10, 15, 20, 25, 30, 35,40 nucleotides in length, including all integers and ranges therebetween.

Moreover, the large number of different probes can occupy a relativelysmall area providing a high density array having a probe density ofgenerally greater than about 60, 100, 600, 1000, 5,000, 10,000, 40,000,100,000, or 400,000 different oligonucleotide probes per cm². Thesurface area of the array can be about or less than about 1, 1.6, 2, 3,4, 5, 6, 7, 8, 9, or 10 cm².

Moreover, a person of ordinary skill in the art could readily analyzedata generated using an array. Such protocols are disclosed herein ormay be found in, for example, WO 9743450; WO 03023058; WO 03022421; WO03029485; WO 03067217; WO 03066906; WO 03076928; WO 03093810; WO03100448A1, all of which are specifically incorporated by reference.

B. Sample Preparation

It is contemplated that the miRNA of a wide variety of samples can beanalyzed using arrays, miRNA probes, or array technology. Whileendogenous miRNA is contemplated for use with compositions and methodsdisclosed herein, recombinant miRNA—including nucleic acids that arecomplementary or identical to endogenous miRNA or precursor miRNA—canalso be handled and analyzed as described herein. Samples may bebiological samples, in which case, they can be from biopsy, fine needleaspirates, exfoliates, blood, tissue, organs, semen, saliva, tears,other bodily fluid, hair follicles, skin, or any sample containing orconstituting biological cells. In certain embodiments, samples may be,but are not limited to, fresh, frozen, fixed, formalin fixed, paraffinembedded, or formalin fixed and paraffin embedded. Alternatively, thesample may not be a biological sample, but a chemical mixture, such as acell-free reaction mixture (which may contain one or more biologicalenzymes).

C. Hybridization

After an array or a set of miRNA probes is prepared and the miRNA in thesample is labeled, the population of target nucleic acids is contactedwith the array or probes under hybridization conditions, where suchconditions can be adjusted, as desired, to provide for an optimum levelof specificity in view of the particular assay being performed. Suitablehybridization conditions are well known to those of skill in the art andreviewed in Sambrook et al. (2001) and WO 95/21944. Of particularinterest in embodiments is the use of stringent conditions duringhybridization. Stringent conditions are known to those of skill in theart.

It is specifically contemplated that a single array or set of probes maybe contacted with multiple samples. The samples may be labeled withdifferent labels to distinguish the samples. For example, a single arraycan be contacted with a tumor tissue sample labeled with Cy3, and normaltissue sample labeled with Cy5. Differences between the samples forparticular miRNAs corresponding to probes on the array can be readilyascertained and quantified.

The small surface area of the array permits uniform hybridizationconditions, such as temperature regulation and salt content. Moreover,because of the small area occupied by the high density arrays,hybridization may be carried out in extremely small fluid volumes (e.g.,about 250 μl or less, including volumes of about or less than about 5,10, 25, 50, 60, 70, 80, 90, 100 μl, or any range derivable therein). Insmall volumes, hybridization may proceed very rapidly.

D. Differential Expression Analyses

Arrays can be used to detect differences between two samples.Specifically contemplated applications include identifying and/orquantifying differences between miRNA from a sample that is normal andfrom a sample that is not normal, between a cancerous condition and anon-cancerous condition, or between two differently treated samples.Also, miRNA may be compared between a sample believed to be susceptibleto a particular disease or condition and one believed to be notsusceptible or resistant to that disease or condition. A sample that isnot normal is one exhibiting phenotypic trait(s) of a disease orcondition or one believed to be not normal with respect to that diseaseor condition. It may be compared to a cell that is normal with respectto that disease or condition. Phenotypic traits include symptoms of, orsusceptibility to, a disease or condition of which a component is or mayor may not be genetic or caused by a hyperproliferative or neoplasticcell or cells.

An array comprises a solid support with nucleic acid probes attached tothe support. Arrays typically comprise a plurality of different nucleicacid probes that are coupled to a surface of a substrate in different,known locations. These arrays, also described as “microarrays” orcolloquially “chips” have been generally described in the art, forexample, U.S. Pat. Nos. 5,143,854, 5,445,934, 5,744,305, 5,677,195,6,040,193, 5,424,186 and Fodor et al., 1991), each of which isincorporated by reference in its entirety for all purposes. These arraysmay generally be produced using mechanical synthesis methods or lightdirected synthesis methods that incorporate a combination ofphotolithographic methods and solid phase synthesis methods. Techniquesfor the synthesis of these arrays using mechanical synthesis methods aredescribed in, e.g., U.S. Pat. No. 5,384,261, incorporated herein byreference in its entirety. Although a planar array surface is used incertain aspects, the array may be fabricated on a surface of virtuallyany shape or even a multiplicity of surfaces. Arrays may be nucleicacids on beads, gels, polymeric surfaces, fibers such as fiber optics,glass or any other appropriate substrate (see U.S. Pat. Nos. 5,770,358,5,789,162, 5,708,153, 6,040,193 and 5,800,992, each of which is herebyincorporated in its entirety). Arrays may be packaged in such a manneras to allow for diagnostics or other manipulation of an all inclusivedevice (see for example, U.S. Pat. Nos. 5,856,174 and 5,922,591, eachincorporated in its entirety by reference). See also U.S. patentapplication Ser. No. 09/545,207, filed Apr. 7, 2000, which isincorporated by reference in its entirety for additional informationconcerning arrays, their manufacture, and their characteristics,

Particularly, arrays can be used to evaluate samples with respect todiseases or conditions that include, but are not limited to: chronicpancreatitis; pancreatic cancer; AIDS, autoimmune diseases (rheumatoidarthritis, multiple sclerosis, diabetes—insulin-dependent andnon-independent, systemic lupus erythematosus and Graves disease);cancer (e.g., malignant, benign, metastatic, precancer); cardiovasculardiseases (heart disease or coronary artery disease, stroke—ischemic andhemorrhagic, and rheumatic heart disease); diseases of the nervoussystem; and infection by pathogenic microorganisms (Athlete's Foot,Chickenpox, Common cold, Diarrheal diseases, Flu, Genital herpes,Malaria, Meningitis, Pneumonia, Sinusitis, Skin diseases, Strep throat,Tuberculosis, Urinary tract infections, Vaginal infections, Viralhepatitis); inflammation (allergy, asthma); prion diseases (e.g., CJD,kuru, GSS, FFI).

Moreover, miRNAs can be evaluated with respect to the followingdiseases, conditions, and disorders: pancreatitis, chronic pancreatitis,and/or pancreatic cancer.

Cancers that may be evaluated by the disclosed methods and compositionsinclude cancer cells particularly from the pancreas, includingpancreatic ductal adenocarcinoma (PDAC), but may also include cells andcancer cells from the bladder, blood, bone, bone marrow, brain, breast,colon, esophagus, gastrointestine, gum, head, kidney, liver, lung,nasopharynx, neck, ovary, prostate, skin, stomach, testis, tongue, oruterus. In addition, the cancer may specifically be of the followinghistological type, though it is not limited to these: neoplasm,malignant; carcinoma; carcinoma, undifferentiated; giant and spindlecell carcinoma; small cell carcinoma; papillary carcinoma; squamous cellcarcinoma; lymphoepithelial carcinoma; basal cell carcinoma; pilomatrixcarcinoma; transitional cell carcinoma; papillary transitional cellcarcinoma; adenocarcinoma; gastrinoma, malignant; cholangiocarcinoma;hepatocellular carcinoma; combined hepatocellular carcinoma andcholangiocarcinoma; trabecular adenocarcinoma; adenoid cystic carcinoma;adenocarcinoma in adenomatous polyp; adenocarcinoma, familial polyposiscoli; solid carcinoma; carcinoid tumor, malignant; branchiolo-alveolaradenocarcinoma; papillary adenocarcinoma; chromophobe carcinoma;acidophil carcinoma; oxyphilic adenocarcinoma; basophil carcinoma; clearcell adenocarcinoma; granular cell carcinoma; follicular adenocarcinoma;papillary and follicular adenocarcinoma; nonencapsulating sclerosingcarcinoma; adrenal cortical carcinoma; endometroid carcinoma; skinappendage carcinoma; apocrine adenocarcinoma; sebaceous adenocarcinoma;ceruminous adenocarcinoma; mucoepidermoid carcinoma; cystadenocarcinoma;papillary cystadenocarcinoma; papillary serous cystadenocarcinoma;mucinous cystadenocarcinoma; mucinous adenocarcinoma; signet ring cellcarcinoma; infiltrating duct carcinoma; medullary carcinoma; lobularcarcinoma; inflammatory carcinoma; paget's disease, mammary; acinar cellcarcinoma; adenosquamous carcinoma; adenocarcinoma w/squamousmetaplasia; thymoma, malignant; ovarian stromal tumor, malignant;thecoma, malignant; granulosa cell tumor, malignant; androblastoma,malignant; sertoli cell carcinoma; Leydig cell tumor, malignant; lipidcell tumor, malignant; paraganglioma, malignant; extra-mammaryparaganglioma, malignant; pheochromocytoma; glomangiosarcoma; malignantmelanoma; amelanotic melanoma; superficial spreading melanoma; maligmelanoma in giant pigmented nevus; epithelioid cell melanoma; bluenevus, malignant; sarcoma; fibrosarcoma; fibrous histiocytoma,malignant; myxosarcoma; liposarcoma; leiomyosarcoma; rhabdomyosarcoma;embryonal rhabdomyosarcoma; alveolar rhabdomyosarcoma; stromal sarcoma;mixed tumor, malignant; mullerian mixed tumor; nephroblastoma;hepatoblastoma; carcinosarcoma; mesenchymoma, malignant; brenner tumor,malignant; phyllodes tumor, malignant; synovial sarcoma; mesothelioma,malignant; dysgerminoma; embryonal carcinoma; teratoma, malignant;struma ovarii, malignant; choriocarcinoma; mesonephroma, malignant;hemangiosarcoma; hemangioendothelioma, malignant; kaposi's sarcoma;hemangiopericytoma, malignant; lymphangiosarcoma; osteosarcoma;juxtacortical osteosarcoma; chondrosarcoma; chondroblastoma, malignant;mesenchymal chondrosarcoma; giant cell tumor of bone; ewing's sarcoma;odontogenic tumor, malignant; ameloblastic odontosarcoma; ameloblastoma,malignant; ameloblastic fibrosarcoma; pinealoma, malignant; chordoma;glioma, malignant; ependymoma; astrocytoma; protoplasmic astrocytoma;fibrillary astrocytoma; astroblastoma; glioblastoma; oligodendroglioma;oligodendroblastoma; primitive neuroectodermal; cerebellar sarcoma;ganglioneuroblastoma; neuroblastoma; retinoblastoma; olfactoryneurogenic tumor; meningioma, malignant; neurofibrosarcoma;neurilemmoma, malignant; granular cell tumor, malignant; malignantlymphoma; Hodgkin's disease; Hodgkin's lymphoma; paragranuloma;malignant lymphoma, small lymphocytic; malignant lymphoma, large cell,diffuse; malignant lymphoma, follicular; mycosis fungoides; otherspecified non-Hodgkin's lymphomas; malignant histiocytosis; multiplemyeloma; mast cell sarcoma; immunoproliferative small intestinaldisease; leukemia; lymphoid leukemia; plasma cell leukemia;erythroleukemia; lymphosarcoma cell leukemia; myeloid leukemia;basophilic leukemia; eosinophilic leukemia; monocytic leukemia; mastcell leukemia; megakaryoblastic leukemia; myeloid sarcoma; and hairycell leukemia. Moreover, miRNAs can be evaluated in precancers, such asmetaplasia, dysplasia, and hyperplasia.

It is specifically contemplated that the disclosed methods andcompositions can be used to evaluate differences between stages ofdisease, such as between hyperplasia, neoplasia, pre-cancer and cancer,or between a primary tumor and a metastasized tumor.

Moreover, it is contemplated that samples that have differences in theactivity of certain pathways may also be compared. These pathwaysinclude the following and those involving the following factors:antibody response, apoptosis, calcium/NFAT signaling, cell cycle, cellmigration, cell adhesion, cell division, cytokines and cytokinereceptors, drug metabolism, growth factors and growth factor receptors,inflammatory response, insulin signaling, NFκ-B signaling, angiogenesis,adipogenesis, cell adhesion, viral infecton, bacterial infection,senescence, motility, glucose transport, stress response, oxidation,aging, telomere extension, telomere shortening, neural transmission,blood clotting, stem cell differentiation, G-Protein Coupled Receptor(GPCR) signaling, and p53 activation.

Cellular pathways that may be profiled also include but are not limitedto the following: any adhesion or motility pathway including but notlimited to those involving cyclic AMP, protein kinase A, G-proteincouple receptors, adenylyl cyclase, L-selectin, E-selectin, PECAM,VCAM-1, α-actinin, paxillin, cadherins, AKT, integrin-α, integrin-β,RAF-1, ERK, PI-3 kinase, vinculin, matrix metalloproteinases, RhoGTPases, p85, trefoil factors, profilin, FAK, MAP kinase, Ras, caveolin,calpain-1, calpain-2, epidermal growth factor receptor, ICAM-1, ICAM-2,cofilin, actin, gelsolin, RhoA, RAC1, myosin light chain kinase,platelet-derived growth factor receptor or ezrin; any apoptosis pathwayincluding but not limited to those involving AKT, Fas ligand, NFκB,caspase-9, PI3 kinase, caspase-3, caspase-7, ICAD, CAD, EndoG, GranzymeB, Bad, Bax, Bid, Bak, APAF-1, cytochrome C, p53, ATM, Bcl-2, PARP,Chk1, Chk2, p21, c-Jun, p73, Rad51, Mdm2, Rad50, c-Abl, BRCA-1,perforin, caspase-4, caspase-8, caspase-6, caspase-1, caspase-2,caspase-10, Rho, Jun kinase, Jun kinase kinase, Rip2, lamin-A, lamin-B1,lamin-B2, Fas receptor, H₂O₂, Granzyme A, NADPH oxidase, HMG2, CD4,CD28, CD3, TRADD, IKK, FADD, GADD45, DR3 death receptor, DR4/5 deathreceptor, FLIPs, APO-3, GRB2, SHC, ERK, MEK, RAF-1, cyclic AMP, proteinkinase A, E2F, retinoblastoma protein, Smac/Diablo, ACH receptor,14-3-3, FAK, SODD, TNF receptor, RIP, cyclin-D1, PCNA, Bcl-XL, PIP2,PIP3, PTEN, ATM, Cdc2, protein kinase C, calcineurin, IKKα, IKKβ, IKKγ,SOS-1, c-FOS, Traf-1, Traf-2, IκB1313 or the proteasome; any cellactivation pathway including but not limited to those involving proteinkinase A, nitric oxide, caveolin-1, actin, calcium, protein kinase C,Cdc2, cyclin B, Cdc25, GRB2, SRC protein kinase, ADP-ribosylationfactors (ARFs), phospholipase D, AKAP95, p68, Aurora B, CDK1, Eg7,histone H3, PKAc, CD80, PI3 kinase, WASP, Arp2, Arp3, p16, p34, p20,PP2A, angiotensin, angiotensin-converting enzyme, protease-activatedreceptor-1, protease-activated receptor-4, Ras, RAF-1, PLCβ, PLCγ,COX-1, G-protein-coupled receptors, phospholipase A2, IP3, SUMO1, SUMO2/3, ubiquitin, Ran, Ran-GAP, Ran-GEF, p53, glucocorticoids,glucocorticoid receptor, components of the SWI/SNF complex, RanBP1,RanBP2, importins, exportins, RCC1, CD40, CD40 ligand, p38, IKKα, IKKβ,NFκB, TRAF2, TRAF3, TRAF5, TRAF6, IL-4, IL-4 receptor, CDK5, AP-1transcription factor, CD45, CD4, T cell receptors, MAP kinase, nervegrowth factor, nerve growth factor receptor, c-Jun, c-Fos, Jun kinase,GRB2, SOS-1, ERK-1, ERK, JAK2, STAT4, IL-12, IL-12 receptor, nitricoxide synthase, TYK2, IFNγ, elastase, IL-8, epithelins, IL-2, IL-2receptor, CD28, SMAD3, SMAD4, TGFβ or TGFβ receptor; any cell cycleregulation, signaling or differentiation pathway including but notlimited to those involving TNFs, SRC protein kinase, Cdc2, cyclin B,Grb2, Sos-1, SHC, p68, Aurora kinases, protein kinase A, protein kinaseC, Eg7, p53, cyclins, cyclin-dependent kinases, neural growth factor,epidermal growth factor, retinoblastoma protein, ATF-2, ATM, ATR, AKT,CHK1, CHK2, 14-3-3, WEE1, CDC25 CDC6, Origin Recognition Complexproteins, p15, p16, p27, p21, ABL, c-ABL, SMADs, ubiquitin, SUMO, heatshock proteins, Wnt, GSK-3, angiotensin, p73 any PPAR, TGFα, TGFβ, p300,MDM2, GADD45, Notch, cdc34, BRCA-1, BRCA-2, SKP1, the proteasome, CUL1,E2F, p107, steroid hormones, steroid hormone receptors, IκBα, IκBβ,Sin3A, heat shock proteins, Ras, Rho, ERKs, IKKs, PI3 kinase, Bcl-2,Bax, PCNA, MAP kinases, dynein, RhoA, PKAc, cyclin AMP, FAK, PIP2, PIP3,integrins, thrombopoietin, Fas, Fas ligand, PLK3, MEKs, JAKs, STATs,acetylcholine, paxillin calcineurin, p38, importins, exportins, Ran,Rad50, Rad51, DNA polymerase, RNA polymerase, Ran-GAP, Ran-GEF, NuMA,Tpx2, RCC1, Sonic Hedgehog, Crm1, Patched (Ptc-1), MPF, CaM kinases,tubulin, actin, kinetochore-associated proteins, centromere-bindingproteins, telomerase, TERT, PP2A, c-MYC, insulin, T cell receptors, Bcell receptors, CBP, IKβ, NFκB, RAC1, RAF1, EPO, diacylglycerol, c-Jun,c-Fos, Jun kinase, hypoxia-inducible factors, GATA4, β-catenin,α-catenin, calcium, arrestin, survivin, caspases, procaspases, CREB,CREM, cadherins, PECAMs, corticosteroids, colony-stimulating factors,calpains, adenylyl cyclase, growth factors, nitric oxide, transmembranereceptors, retinoids, G-proteins, ion channels, transcriptionalactivators, transcriptional coactivators, transcriptional repressors,interleukins, vitamins, interferons, transcriptional corepressors, thenuclear pore, nitrogen, toxins, proteolysis, or phosphorylation; or anymetabolic pathway including but not limited to those involving thebiosynthesis of amino acids, oxidation of fatty acids, biosynthesis ofneurotransmitters and other cell signaling molecules, biosynthesis ofpolyamines, biosynthesis of lipids and sphingolipids, catabolism ofamino acids and nutrients, nucleotide synthesis, eicosanoids, electrontransport reactions, ER-associated degradation, glycolysis,fibrinolysis, formation of ketone bodies, formation of phagosomes,cholesterol metabolism, regulation of food intake, energy homeostasis,prothrombin activation, synthesis of lactose and other sugars,multi-drug resistance, biosynthesis of phosphatidylcholine, theproteasome, amyloid precursor protein, Rab GTPases, starch synthesis,glycosylation, synthesis of phoshoglycerides, vitamins, the citric acidcycle, IGF-1 receptor, the urea cycle, vesicular transport, or salvagepathways. It is further contemplated that the disclosed nucleic acidsmolecules can be employed in diagnostic and therapeutic methods withrespect to any of the above pathways or factors. Thus, in someembodiments, a miRNA may be differentially expressed with respect to oneor more of the above pathways or factors.

Phenotypic traits also include characteristics such as longevity,morbidity, appearance (e.g., baldness, obesity), strength, speed,endurance, fertility, susceptibility or receptivity to particular drugsor therapeutic treatments (drug efficacy), and risk of drug toxicity.Samples that differ in these phenotypic traits may also be evaluatedusing the arrays and methods described.

In certain embodiments, miRNA profiles may be generated to evaluate andcorrelate those profiles with pharmacokinetics. For example, miRNAprofiles may be created and evaluated for patient tumor and bloodsamples prior to the patient being treated or during treatment todetermine if there are miRNAs whose expression correlates with theoutcome of the patient. Identification of differential miRNAs can leadto a diagnostic assay involving them that can be used to evaluate tumorand/or blood samples to determine what drug regimen the patient shouldbe provided. In addition, identification of differential miRNAs can beused to identify or select patients suitable for a particular clinicaltrial. If a miRNA profile is determined to be correlated with drugefficacy or drug toxicity, such may be relevant to whether that patientis an appropriate patient for receiving a drug or for a particulardosage of a drug.

In addition to the above prognostic assays, blood samples from patientswith a variety of diseases can be evaluated to determine if differentdiseases can be identified based on blood miRNA levels. A diagnosticassay can be created based on the profiles that doctors can use toidentify individuals with a disease or who are at risk to develop adisease. Alternatively, treatments can be designed based on miRNAprofiling. Examples of such methods and compositions are described inthe U.S. Provisional Patent Application entitled “Methods andCompositions Involving miRNA and miRNA Inhibitor Molecules” filed on May23, 2005, in the names of David Brown, Lance Ford, Angie Cheng and RichJarvis, which is hereby incorporated by reference in its entirety.

E. Other Assays

In addition to the use of arrays and microarrays, it is contemplatedthat a number of different assays could be employed to analyze miRNAs,their activities, and their effects. Such assays include, but are notlimited to, nucleic acid amplification, polymerase chain reaction,quantitative PCR, RT-PCR, in situ hybridization, Northern hybridization,hybridization protection assay (HPA), branched DNA (bDNA) assay, rollingcircle amplification (RCA), single molecule hybridization detection,Invader assay, and/or Bridge Litigation Assay.

F. Evaluation of Expression Levels and Diff Pair Values

A variety of different models can be employed to evaluate expressionlevels and/or other comparative values based on expression levels ofmiRNAs (or their precursors or targets). One model is a logisticregression model (see the Wikipedia entry on the World Wide Web aten.wikipedia.com, which is hereby incorporated by reference).

Start by computing the weighted sum of the DiffPair values:

z=β ₀+β₁*Diff(miR_(1a),miR_(1b))+β₂*Diff(miR_(2a),miR_(2b))+

where the β₀ is the (Intercept) term identified in the spreadsheets,while the remaining β_(i) are the weights corresponding to the variousDiffPairs in the model in question. Once z is computed, the scorep_(malignant) (which may be interpreted as predicted probability ofmalignancy) is calculated as

$p_{malignant} = \frac{1}{1 + {\exp \left( {- z} \right)}}$

This functions to turn the number z, which may be any value fromnegative infinity to positive infinity, into a number between 0 and 1,with negative values for z becoming scores/probabilities of less than50% and positive values for z becoming scores/probabilities of greaterthan 50%.

Other examples of models include but are not limited to Decision Tree,Linear Disciminant Analysis, Neural Network, Support Vector Machine, andk-Nearest Neighbor Classifier. In certain embodiments, a scoringalgorithm comprises a method selected from the group consisting of:Linear Discriminate Analysis (LDA), Significance Analysis ofMicroarrays, Tree Harvesting, CART, MARS, Self Organizing Maps, FrequentItem Set, Bayesian networks, Prediction Analysis of Microarray (PAM),SMO, Simple Logistic Regression, Logistic Regression, MultilayerPerceptron, Bayes Net, Naive Bayes, Naive Bayes Simple, Naive Bayes Up,IB1, Ibk, Kstar, LWL, AdaBoost, ClassViaRegression, Decorate, MulticlassClassifier, Random Committee, j48, LMT, NBTree, Part, Random Forest,Ordinal Classifier, Sparse Linear Programming (SPLP), Sparse LogisticRegression (SPLR), Elastic NET, Support Vector Machine, Prediction ofResidual Error Sum of Squares (PRESS), and combinations thereof. Aperson of ordinary skill in the art could use these different models toevaluate expression level data and comparative data involving expressionlevels of one or more miRs (or their precursors or their targets). Insome embodiments, the underlying classification algorithm is lineardiscriminate analysis (LDA). LDA has been extensively studied in themachine learning literature, for example, Hastie et al. (2009) andVenables & Ripley (2002), which are both incorporated by reference.

Models may take into account one or more diff pair values or they mayalso take into account differential expression of one or more miRNAs notspecifically as part of a diff pair. A diagnostic or risk score may bebased on 1, 2, 3, 4, 5, 6, 7, 8 or more diff pair values (or any rangederivable therein), but in some embodiments, it takes into accountadditionally or alternatively, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or moremiRNA expression levels (or any range derivable therein), wherein themiRNA expression level detectably differs between PDAC cells and cellsthat are not PDAC.

In some embodiments, a score is prepared. The score may involve numberssuch as 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, (or any range or a subsettherein) in some embodiments.

IV. KITS

Any of the compositions described herein may be comprised in a kit. In anon-limiting example, reagents for isolating miRNA, labeling miRNA,and/or evaluating a miRNA population using an array, nucleic acidamplification, and/or hybridization can be included in a kit, as well asreagents for preparation of samples from pancreatic samples. The kit mayfurther include reagents for creating or synthesizing miRNA probes. Suchkits may thus comprise, in suitable container means, an enzyme forlabeling the miRNA by incorporating labeled nucleotides or unlabelednucleotides that are subsequently labeled. In certain aspects, the kitcan include amplification reagents. In other aspects, the kit mayinclude various supports, such as glass, nylon, polymeric beads, and thelike, and/or reagents for coupling any probes and/or target nucleicacids. Kits may also include one or more buffers, such as a reactionbuffer, labeling buffer, washing buffer, or hybridization buffer,compounds for preparing the miRNA probes, and components for isolatingmiRNAs. Other kits may include components for making a nucleic acidarray comprising miRNAs, and thus, may include, for example, a solidsupport.

Kits for implementing methods described herein are specificallycontemplated. In some embodiments, there are kits for preparing miRNAsfor multi-labeling and kits for preparing miRNA probes and/or miRNAarrays. In such embodiments, kits comprise, in suitable container means,1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more of the following: 1)poly(A) polymerase; 2) unmodified nucleotides (G, A, T, C, and/or U); 3)a modified nucleotide (labeled or unlabeled); 4) poly(A) polymerasebuffer; 5) at least one microfilter; 6) label that can be attached to anucleotide; 7) at least one miRNA probe; 8) reaction buffer; 9) a miRNAarray or components for making such an array; 10) acetic acid; 11)alcohol; 12) solutions for preparing, isolating, enriching, andpurifying miRNAs or miRNA probes or arrays. Other reagents include thosegenerally used for manipulating RNA, such as formamide, loading dye,ribonuclease inhibitors, and DNase.

In specific embodiments, kits include an array containing miRNA probes,as described in the application. An array may have probes correspondingto all known miRNAs of an organism or a particular tissue or organ inparticular conditions, or to a subset of such probes. The subset ofprobes on arrays may be or include those identified as relevant to aparticular diagnostic, therapeutic, or prognostic application. Forexample, the array may contain one or more probes that are indicative orsuggestive of 1) a disease or condition (chronic pancreatitis and/orpancreatic cancer), 2) susceptibility or resistance to a particular drugor treatment; 3) susceptibility to toxicity from a drug or substance; 4)the stage of development or severity of a disease or condition(prognosis); and 5) genetic predisposition to a disease or condition.

For any kit embodiment, including an array, there can be nucleic acidmolecules that contain or can be used to amplify a sequence that is avariant of, identical to, or complementary to all or part of any of theSEQ ID NOs disclosed herein. In certain embodiments, a kit or array cancontain one or more probes for the miRNAs identified by SEQ ID NOsdisclosed herein. Any nucleic acid discussed above may be implemented aspart of a kit.

Components of kits may be packaged either in aqueous media or inlyophilized form. The container means of the kits will generally includeat least one vial, test tube, flask, bottle, syringe, or other containermeans, into which a component may be placed, and preferably, suitablyaliquotted. Where there is more than one component in the kit (e.g.,labeling reagent and label may be packaged together), the kit also willgenerally contain a second, third, or other additional container intowhich the additional components may be separately placed. However,various combinations of components may be comprised in a vial. The kitsalso may include a means for containing the nucleic acids, and any otherreagent containers in close confinement for commercial sale. Suchcontainers may include injection or blow molded plastic containers intowhich the desired vials are retained.

When the components of a kit are provided in one and/or more liquidsolutions, the liquid solution may be an aqueous solution, with asterile aqueous solution being particularly preferred.

However, the components of a kit may be provided as dried powder(s).When reagents and/or components are provided as a dry powder, the powdercan be reconstituted by the addition of a suitable solvent. It isenvisioned that the solvent may also be provided in another containermeans. In some embodiments, labeling dyes are provided as a dried power.It is contemplated that 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120,120, 130, 140, 150, 160, 170, 180, 190, 200, 300, 400, 500, 600, 700,800, 900, 1000 μg, or at least or at most those amounts, of dried dyeare provided in kits. The dye may then be resuspended in any suitablesolvent, such as DMSO.

The container means will generally include at least one vial, test tube,flask, bottle, syringe and/or other container means, into which thenucleic acid formulations are placed, for example, suitably allocated.Kits may also comprise a second container means for containing asterile, pharmaceutically acceptable buffer and/or other diluent.

Kits may include a means for containing the vials in close confinementfor commercial sale, such as, e.g., injection and/or blow-molded plasticcontainers into which the desired vials are retained.

Such kits may also include components that facilitate isolation of thelabeled miRNA. It may also include components that preserve or maintainthe miRNA or that protect against its degradation. Such components maybe RNAse-free or protect against RNAses. Such kits generally willcomprise, in suitable means, distinct containers for each individualreagent or solution.

A kit may also include instructions for employing the kit components aswell the use of any other reagent not included in the kit. Instructionsmay include variations that can be implemented.

Kits may also include one or more of the following: control RNA;nuclease-free water; RNase-free containers, such as 1.5 ml tubes;RNase-free elution tubes; PEG or dextran; ethanol; acetic acid; sodiumacetate; ammonium acetate; guanidinium; detergent; nucleic acid sizemarker; RNase-free tube tips; and RNase or DNase inhibitors.

It is contemplated that such reagents are embodiments of kits. Suchkits, however, are not limited to the particular items identified aboveand may include any reagent used for the manipulation orcharacterization of miRNA.

V. EXAMPLES

The following examples are included to demonstrate preferred embodimentsof the invention. It should be appreciated by those of skill in the artthat the techniques disclosed in the examples that follow representtechniques discovered by the inventors to function well in the practiceof the invention, and thus can be considered to constitute preferredmodes for its practice. However, those of skill in the art will, inlight of the present disclosure, appreciate that many changes can bemade in the specific embodiments that are disclosed and still obtain alike or similar result without departing from the spirit and scope ofthe invention.

Example 1

A panel of 95 formalin-fixed, paraffin-embedded (FFPE) tissue specimens(52 pancreatic ductal adenocarcinoma (PDAC) samples and 43 chronicpancreatitis (CP) samples) was used as a training set for building amodel that can distinguish between PDAC samples and CP samples. A modelis a classifier trained on a number of miRNA DiffPairs to predictwhether a specimen is a PDAC specimen. The variables (factors) for themodel are: the classification algorithm (e.g., Linear DiscriminateAnalysis (LDA), Partial Least Squares (PLS), and Logistic Regression);the number of miRNA DiffPairs included in the analysis; the method toselect the number of miRNA DiffPairs (e.g., Wilcox-test, RankProduct,and Moderated t-test). All such methods are probabilistic in nature andprovide the posterior probability of a sample being PDAC, so all scoresare 0 to 1 inclusive. Models were evaluated and ranked based onestimates of area under the receiver operating characteristic curve(AUC) as determined from nested and replicated 5-fold cross-validation.Additional performance metrics such as Matthew's correlation coefficientand Youden's index were also considered.

Table 1 provides representative miRNAs that may be included in a miRNADiffPair. Sequences and ABI part numbers (ABI, Foster City, Calif.) areprovided.

TABLE 1  Representative miRNAs ABI Part SEQ miRNA No. Sequence ID NO.miR-196a 000495 UAGGUAGUUUCAUGUUGUUGG 1 miR-210 000512CUGUGCGUGUGACAGCGGCUGA 2 miR-217 002337 UACUGCAUCAGGAACUGAUUGGA 3miR-375 000564 UUUGUUCGUUCGGCUCGCGUGA 4 miR-130 000456CAGUGCAAUGAUGAAAGGGCAU 5 miR-135b 000461 UAUGGCUUUUCAUUCCUAUGUG 6miR-148a 000470 UCAGUGCACUACAGAACUUUGU 7 miR-155 000479UUAAUGCUAAUCGUGAUAGGGG 8 miR-223 000526 UGUCAGUUUGUCAAAUACCCC 9 miR-96000434 UUUGGCACUAGCACAUUUUUGC 10 miR-24 000402 UGGCUCAGUUCAGCAGGAACAG 11miR-21 000397 UAGCUUAUCAGACUGAUGUUGA 12

Based on the ranking from the AUC estimate, a single model was selectedfor validation on 162 annotated FNA samples. These FNA samples werecollected from eight independent sites in North America and Europe.Samples were collected by performing Endoscopic Ultrasound Guided FineNeedle Aspiration (EUS-FNA) in patients showing evidence of solidpancreatic lesions that were referred for cytological evaluation due tosuspicion of pancreatic ductal adenocarcinoma. Patients were informed ofthe study, given the opportunity to participate in the study, andcompleted informed consent forms. The study and consent forms wereapproved by each institution's Institutional Review Board or EthicsCommittee.

Patients were selected for inclusion in this study based on thefollowing criteria: (a) pancreatic EUS-FNA is indicated for diagnosticworkup based on standard of care and determined to be essential for thepatient's clinical care by the Gastroenterologist taking care of thepatient; (b) the patient is age 18 or older; and (c) the patient may beany gender or ethnicity to be included in the study. Patients wereexcluded from the study based on the following criteria: (a) evidence ofother active primary cancer (non-pancreatic); (b) the patient is under18 years of age; or (c) the study physicians determined that sufficientEUS-FNA material cannot be obtained.

After collection of the diagnostic FNA for cytology evaluation, eachparticipant had one to three additional FNAs collected and depositedinto Asuragen's RNARetain® pre-analytical RNA Stabilization Solution.Samples were stored in RNARetain overnight at 4° C. and then stored at−80° C. and shipped to Asuragen on dry ice in batches, where they werestored at −80 until processing for RNA isolation.

RNA isolation was performed using a modified procedure based on themirVana PARIS kit (Ambion), and reverse transcription was performed. Foreach sample, 30 ng of RNA per RT reaction per miRNA was used as input,and each sample was performed in triplicate if sufficient total RNA wasavailable. Sample RNA concentration was adjusted to 7.5 ng/uL. RT MasterMix was prepared using TaqMan MicroRNA RT master kit components andindividual TaqMan Assay RT primers. Following reverse transcription,quantitative PCR (qPCR) was performed to assess the expression levels ofthe miRNAs listed in Table 1.

The model that provided the best overall performance in terms of itsability to distinguish between PDAC and CP samples was the LDA+ModT+6model and included 6 miRNA DiffPairs: Diff(miR-135b,miR-24);Diff(miR-130b,miR-135b); Diff(miR-135b,miR-148a);Diff(miR-375,miR-135b); Diff(miR-135b,miR-96); andDiff(miR-148a,miR-196a). As explained above, a miRNA DiffPair is abiomarker that is a self-normalizing combination of two miRNAs withexpression values from one miRNA subtracted from expression values ofanother miRNA. The combination could involve one miRNA as an actualpredictor and another as a normalize, or the combination could involvetwo anti-correlated predictor miRNAs.

Expression values were integrated using Linear Discriminate Analysis(LDA). The implementation of this algorithm is known to those of skillin the art and is, for example, described in “Modern Applied Statisticswith S” by Venables and Ripley. The source code used in the analysis isalso known to those of skill in the art and was adopted from the MASSpackage in the R programming language, which is available on the WorldWide Web at cran.r-project.org/web/packages/MASS/index.html.

LDA integrates the expression values obtained into a single score thatmakes the classification decision (PDAC vs CP (called “Benign”)). Thescore represents the probability of a sample being PDAC based on theexpression data of the diff pairs. Because it is a probability, thescore is 0 to 1 inclusive. The score is dichotomized in order to make aclinical call of a diagnostic positive (predicted PDAC) or a diagnosticnegative (predicted Benign).

As shown in FIG. 1, the LDA+ModT+6 model provided the highest AUC valuein the training exercise using the FFPE samples. The different modelswere ranked by the estimated AUC from cross-validation based on the 95FFPE training samples. The performance metrics from all of the topmodels were similar. However, the LDA+ModT+6 model had nominally higherAUC and MCC estimates (including ties) and a strong Youden index.Specifically, as shown in FIG. 1, that model provided an AUC estimate of0.978; sensitivity of 0.95 (sensitivity=#true positives/(#truepositives+#false negatives)); specificity of 0.93 (specificity=#of truenegatives/(#true negatives+#false positives)); and a Youden's index of0.88 (Youden's index=(sensitivity+specificity)−1). Negative controlmodels (models based on random chance) were also used in the analysis,and the performance of those models had AUC estimates around 0.5 asexpected (data not shown).

The LDA+ModT+6 model was then used to evaluate an independent panel offine needs aspirate (FNA) specimens. The 162 FNA samples (128 PDAC and34 CP from multiple sites) are referred to as the test set, and nosamples from the test set were used to optimize (train) the final model.On the test set, the LDA+ModT+6 model provided an AUC of 0.90 withsensitivity and specificity at 0.89 and 0.91, respectively. The resultsare shown in FIG. 2.

In FIG. 2, panels are stratified horizontally by performance metrics ofLDA+ModT+6, while panels are stratified vertically by data sets(Train=FFPE sample assessment and Test=FNA sample assessment). TheLDA+ModT+6 model shows relatively robust performance estimates across arange of thresholds (x-axis in all plots), for most performance metrics.This suggests that performance is stable and unlikely to be greatlyaffected by threshold estimates. Because the model calculates theposterior probability of being PDAC, the thresholds range from 0 to 1inclusive. The results shown in FIG. 2 confirm that it is possible toset the threshold value for distinguishing between a PDAC and Benignsample over a broad range, and the threshold value can be adjusted toprovide increased sensitivity or specificity as needed. For example, thethreshold may be 0.5, where a sample is a diagnostic positive if thescore is greater than or equal to 0.5, and the sample is a diagnosticnegative if the score is less than 0.5. This 0.5 threshold was used toevaluate the ability of the LDA+ModT+6 model, as well as other models,to correctly distinguish between PDAC and CP samples.

Although the LDA+ModT+6 model (also called the “Full Model” or“miRInform” herein and in the drawings) provided improved sensitivity ascompared to other models, two other models were also able to distinguishbetween PDAC and CP samples. The “Simple Model” evaluated only one miRNADiffPair: miR-135b and miR-24. The “Reduced Model” evaluated four miRNADiffPairs: Diff(miR-135b,miR-24); Diff(miR-130b,miR-135b);Diff(miR-135b,miR-148a); and Diff(miR-148a,miR-196a).

The Simple Model provided adequate specificity and sensitivity, as shownin FIG. 3. In FIG. 3, the differences in the number of samples availablefor classification are due to the handling of missing data. The morecomplex model (LDA+ModT+6) is more sensitive to missingdata—particularly 196a and 96. In this case, any measurable Ct less thanor equal to 45 was set to 40 Ct. Ct values greater than 45 werenon-determined. The sensitivity for the LDA+ModT+6 is statisticallybetter than the sensitivity value for the Simple Model, suggesting thatthe LDA+ModT+6 model maintains high specificity while improving onsensitivity as compared to the Simple Model.

A post-hoc analysis was performed to evaluate the equivalence betweenthe Reduced Model based on 4 miRNA DiffPairs, and the LDA+ModT+6 model(also called the “Full Model”). As shown in FIG. 4, the Reduced Modelmaintained high performance, indicating that fewer than 6 miRNADiffPairs are sufficient to distinguish between PDAC and CP samples. TheReduced Model was trained on the same 95 FFPE sample training set as wasused for the LDA+ModT+6 model. Both the Full and Reduced models wereindividually trained on FFPE samples and tested on FNA samples. ThePearson correlation coefficient (PCC), Spearman rank correlation (SRC)and the concordance correlation coefficient (CCC) are all strong andclose to the maximum value of 1, indicating that both models performedat a high level. In fact, there was a 100% overall agreement between thecalls made by the Reduced Model and those made by the Full Model.

An analysis was also performed to determine how well the Simple, Full,and Reduced Models could distinguish between PDAC and CP in samples thatwere determined to exhibit atypical cytology. These samples were laterresolved as either PDAC or CP based on histological assessment. As shownin FIG. 5, all models performed well in terms of the ability todistinguish between the PDAC and CP samples.

Example 2

miRInform Pancreas (MP) can detect PDAC specimens with 95% specificityand 82% sensitivity. However, in certain contexts it is important tohave greater sensitivity or to have an attached measure of confidencethat will stratify MP calls into high confidence and low confidenceresults. In this report, we describe how such a test can be implemented.

Methodology

The original MP test was developed based on building a model on 95 FFPEsamples. The classification algorithm for MP is based on lineardiscriminate analysis that integrates the expression values from 6miRNAs. FIG. 6 captures the performance of MP without adjustment by apotential reflex test or additional QC procedures. These results arebased on the independent validation set of 184 FNA samples, and setsexpectations for unbiased performance estimates. Future consideration ofa reflex test (or associated measures of confidence) should be comparedagainst these baseline results.

One observation from the predictive performance of MP is the negativepredictive value (NPV) estimate. Any additional interpretation ormodification of MP must mitigate the false negative rate, FNR.Basically, the FNR is comparable to the true negative rate, TNR, thuscreating a low NPV. In order to distinguish TNs from FNs (TNs=truenegatives; FNs=false negatives), inventors performed differentialexpression analysis on the TN (n=19) and FN (n=30) samples (see TN andFN from previous slide).

The differential expression analysis is designed to find one single diffpair (two miRNAs used in conjunction to produce a single expressionvalue) to distinguish TNs from FNs. That analysis focuses only on thesubset of miRNAs that are contained in MP. Both parametric andnon-parametric tests were examined, but there seemed to be insufficientsignal for parametric tests, and the results were relatively wellcorrelated between parametric and non-parametric tests. An alternativestrategy is to look for more than 1 diff pair to distinguish between TNsand FNs. That is done in the context of a tree classification algorithmalthough other classification algorithms can be used.

Both the differential expression analysis and models to predict TNs (asopposed to FNs) are evaluated under replicated cross-validation. Inparticular, inventors chose 10 replications of 10-fold cross-validationproducing 100 replications of all results. That is done to help mitigatebias in the performance estimates and to look at the stability of theresults.

Results

FIG. 7 shows the top discriminators of TNs and FNs based on all possiblepairings of all 11 miRNAs run in the panel. The results show the toppairs are predominately composed of miR-155 which suggests that miR-155would be an excellent component of discriminating TNs and FNs. However,miR-155 data is not as complete (in other studies outside of the FNAvalidation study) as, say, the 2^(nd) ranked pair,Diff(miR-130b,miR-24). That particular diff pair is composed of twomiRNAs that are in the original MP model but paired with differentmiRNAs. Therefore, it would not require extra experimental work forevaluation and can easily be calculated as part of the standard test.The issue is that the ExpectedFDR value (the trimean of the falsediscovery rate or FDR based on 10 replications of 10 fold crossvalidation) is not as good (not as low) as the top pair. This suggeststhat the top candidates still have a high probability of being a falsepositive. Setting this issue aside inventors examinedDiff(miR-130b,miR-24) more closely.

In order to visualize predictive accuracy of the candidateDiff(miR-130b,miR-24), inventors examined accuracy estimates associatedwith predicting TNs and FNs. FIG. 8 shows the estimated accuracy of thediff pair, Diff(miR-130b,miR-24), (y-axis) at predicting TNs (downtriangles) and FNs (up triangles) as a function of the threshold(x-axis). As the threshold is adjusted, the predictive performanceincreases (decreases) if the number of samples correctly classifiedincreases (decreases). The vertical dashed lines represent the 2.5 and97.5 percentiles of the estimates for the optimal threshold. In otherwords, the optimal threshold is captured between the vertical lines with95% confidence. The two boxes on the right hand side show theperformance of the diff pair using the lower 95% threshold (top box) andthe upper 95% threshold (lower box). Note that these results are onlyfor the TNs and FNs, and not for all 184 samples in the study.Importantly, the results between the two thresholds are quite similar asis also evident by the relatively narrow range of the 95% range of thethresholds.

Using the upper and lower thresholds for Diff(miR-130b,miR-24),inventors look at overall predictive performance stratified by callstatus (See FIG. 9). The left box shows the results using the lower 95%threshold, and the right box shows the upper 95% threshold. In eithercase, the results are very similar. The results from the figure aregenerated as follows. The samples are classified 1^(st) by MP. Theresults of MP are then stratified by the results ofDiff(miR-130b,miR-24). We observe the samples that are above thethreshold (lower parts of both boxes) tending to be enriched for highconfident calls. That is, we have good sensitivity and specificity forsamples that have Diff(miR-130b,miR-24)>Threshold where Threshold can bethe upper or lower 95% thresholds derived from the previous slide. Incontrast, samples below either threshold tend to be equally balanced fortrue positives (TPs) and FNs. In the end, Samples classified thatare >=the thresholds are likely higher confident calls that will likelyimprove the NPV. However, fewer samples are available so the 95% CI arenow wider (data not shown), but the lower bound of the 95% CI ofsensitivity and NPV are generally greater than the point estimates fromthe population as a whole.

Inventors considered the results of the following experiment: 1) If thecall is PDAC according to MP, then the sample is called PDAC 2) If thecall is Benign according to MP, the reflex test, Diff(miR-130b,miR-24),classifies specimens as either PDAC (above the threshold) or Benign(below the threshold). In this case, inventors have a range ofthresholds from our simulation so a sample is classified based on thecall from all 100 simulations (10 replicates of 10 foldcross-validation). All samples that do not have the same call of PDAC orBenign by the reflex test for all 100 simulated runs, the sample isautomatically classified incorrectly. The left side of FIG. 10 showsthat most samples are classified consistently, but there is an expectedhit in specificity as inventors are in effect taking calls that wereoriginally classified as Benign and now evaluating with a reflex test.One can only expect this to increase the FPR (decrease specificity), butthe overall accuracy increases as we classify more PDAC samplescorrectly than incorrectly classifying Benign samples. The right side ofFIG. 10 shows the overall performance using the reflex test with MP.Note that the total number of samples is still 184—the same numberconsidered for the FNA validation. This should be compared to FIG. 6where the results are shown without the reflex test. There is noindependent test set for the reflex test so we are training and testingon the same data set which can lead to biased performance estimates.This is mitigated by calling all samples within the 95% threshold rangeas incorrect thereby decreasing overall performance. In other words, allsamples classified in the gray zone spanned by the 95% threshold rangewill only penalize the overall performance of the test.

FIG. 11 juxtaposes the performance of MP with the reflex test (left handside) and without the reflex test (right hand side). As explained in theprevious figure, only samples consistently classified as either PDAC orBenign by the reflex test will be marked as accurately classified. Incontrast to the previous figure, all samples that were PDAC by cytologyare excluded from this analysis so the total specimens considered foranalysis is much less than 184.

Inventors performed an additional analysis where all samples that werePDAC or Suspicious by cytology are excluded from this analysis. Weevaluated this population because samples that were Suspicious bycytology tended to be PDAC samples. This would be another clinicallyrelevant application of the test. FIG. 12 shows performance of MP with(left hand side) and without (right hand side) the reflex test. Asexplained in the previous figure, only samples consistently classifiedas either PDAC or Benign by the reflex test will be accuratelyclassified. In general, we see an increase in overall accuracy includingthe reflex test as we now tend to call more PDAC samples correctly withthe cost of a reduction in specificity.

Inventors evaluated the performance of MP when used in conjunction withCytology. That means a sample is classified as PDAC if either Cytologyor MP classifies the specimen as PDAC. Otherwise, the specimen isclassified as Benign. The term ‘Conjunction’ implies an either/orrelationship of Cytology and MP.

FIG. 13 shows performance using this strategy with (left hand side) andwithout (right hand side) the reflex test. The end result is a test thatis very accurate because of the excellent sensitivity of Cytology andthe excellent specificity of MP. The problem with this strategy is thatthe FPR will increase because Cytology tends to call some samples PDACeven though they are Benign.

The analysis above provides support that the reflex test improvessensitivity at the risk of reducing specificity. If the focus issensitivity, then Diff(miR-130b,miR-24) can be used. If your focus isspecificity, then do not use Diff(miR-130b,miR-24). An alternatestrategy is to use Diff(miR-130b,miR-24) as a concurrent test tostratify results into high quality results and low quality results (SeeFIG. 9). With that strategy in mind about ⅔ of the specimens areclassified very accurately while ⅓ of the specimens are likely to beexcluded from the high confidence results. Two more notes about testresults: overall accuracy estimates will increase withDiff(miR-130b,miR-24) as it focuses on increased sensitivity and thereis a higher prevalence of PDAC samples; pay close attention to the lower95% CI of Sens, Spec, PPV and NPV.

Inventors results outlined above were focused on Diff(miR-130b,miR-24).At this point, inventors will now investigate how the application of amore complex reflex test could affect patient classification results.Specifically, the inventors considered a classification tree based ontwo diff pairs. As per our previous evaluation strategy, inventors willevaluate the classification results (diff pair selection and treeconstruction) using 10 replicates of 10-fold cross-validation in orderto derive a distribution of results. This analysis was performed inorder to see if inventors can improve performance using 2 diff pairsinstead of just Diff(miR-130b,miR-24). FIG. 14 shows how often a givendiff pair was selected to be in a classification tree that discriminatesTN and FN. A diff pair that is selected more frequently than others islikely to have more discriminatory power associated with it. FIG. 15shows the projection of the TNs and FNs projected into the space spannedby the top two diff pairs. One can see the TNs tend to be clustered inthe upper left quadrant while the FNs are predominately in the lowerright. However, in the case of the top predictor,Diff(miR-155,miR-196a), the decision thresholds spanned by the twovertical lines are quite wide which leads to poor overall predictiveperformance. The FN accuracy is 59.33%, the TN accuracy is 44.21% andthe overall accuracy is 53.47%. The performance estimates are low andcan likely be improved by increasing the sample size. In sum,Diff(miR.155,miR.196a) has among the strongest p-value estimates by theWilcox-test, but the threshold is unstable because the 95% range iswide. Diff(miR.130b,miR.24) has a weaker p-value, but more consistentthresholding because the 95% is narrow.

Example 3 Biomarker Performance

Here we discuss the predictive performance of the individual biomarkersas opposed to the predictive model (See Table 2, Table 3 and Table 4 forraw expression values and model scores for the 184 specimens used in thefinal analysis; in Tables 2-4 the sequential numerical identifiers inthe leftmost column refers to the same specimen across tables). First welooked at the inherent ability of the diff pairs in the model toseparate Benign and PDAC samples in both the FFPE (training data) andFNA (test data) samples. This is easily visualized with principalcomponent analysis (PCA) (See FIG. 16). PCA analysis is an unsupervisedmethod of visualization that spatially orients points (samples in thiscase) such that samples with similar expression values are in closerproximity. In other words, the PDAC samples are clustered together andthe Benign samples are clustered together because the underlyingexpression values in the signature capture sufficient information toseparate the classes. Note this analysis is not based on the model builton top of the underlying diff pair expression values, and this methoddoes not use class membership. Next we looked at biomarker expressionindividually. In order to capture the translation of univariatepredictive performance of each DiffPair in MP test, we plotted Youden'sIndex (sensitivity plus specificity minus 1) for each DiffPair as afunction of threshold (See FIG. 17). In general, all marker pairs showedrelatively consistent behavior between sample types exceptDiff(miR-135b,miR-96). As expected, the univariate predictiveperformance decreased between sample types, but, critically, thethresholds of maximum performance were particularly well aligned betweensample types except for Diff(miR-135b,miR-96) and, to a lesser extent,Diff(miR-135b,miR-148a). In aggregate, the DiffPair normalizationprocedure along with robust biomarkers (as measured in the DiffSpace)allowed the model to maintain high performance in an alternate sampletype.

Model Performance

First we interrogated the performance of FNA cytology alone on 184 FNAspecimens. Among these samples, a cytological diagnosis of PDAC wasestablished in 126 cases, benign in 22 cases, atypical in 13 cases,suspicious for adenocarcinoma in 16 cases, and non-diagnostic in 7 cases(FIG. 18). The accuracy of cytological PDAC call was 100%, while abenign cytology call was accurate 63.4% of the time (14/22 specimens),for an overall accuracy of 76.09% (FIG. 19A). Note that the estimate foroverall accuracy assumes that all FNA cytology calls other than PDAC orbenign are miscalls. Since this analysis was completed, one of thosepatients has developed symptoms of progressive pancreatic disease(weight loss, mass infiltration into duodenum, etc.). This patient iscurrently being followed to obtain a more definitive diagnosis. If thispatient develops pancreatic cancer, the accuracy of FNA benign cytologywill decrease to 59.09%. There were 36 specimens in the category ofinconclusive cytology, which could not be categorized as PDAC or benignusing cytology alone. Following the routine preoperative managementalgorithm these patients will undergo a repeat FNA procedure with a hopeof obtaining diagnostic specimen.

Subsequently, we evaluated the performance of the MP test on the samepatient population (See FIG. 18). In the group of 126 PDAC specimens byFNA cytology, the MP test accurately identified 111/126 specimens(88.09%) as PDAC. Within the group of 22 benign specimens by FNAcytology, the MP accurately identified 13 out of 14 true benignspecimens as well as 3 out of 8 true PDAC specimens which were missed bycytology. The one patient which is currently progressing towardpancreatic cancer, was identified as PDAC by MP. In addition, in thegroup of 36 patients with inconclusive cytology (cytology was neitherPDAC nor benign) the test was able to provide a confirmatory diagnosisof PDAC for 20/30 (66.67%) patients with PDAC, and confirm benign in 6/6patients (100%). Overall, the test accurately classified 134 PDACspecimens and 19 benign specimens, for an overall accuracy of 83.15%(See FIG. 19).

We also interrogated the performance of FNA cytology alone on 184 FNAspecimens. Among these samples, a cytological diagnosis of PDAC wasestablished in 126 cases, benign in 22 cases, atypical in 13 cases,suspicious for adenocarcinoma in 16 cases, and non-diagnostic in 7cases. The accuracy of cytological PDAC call was 100%, while a benigncytology call was accurate 63.4% of the time (14/22 specimens), for anoverall accuracy of 76.09% (FIG. 20A). Note that the estimate foroverall accuracy assumes that all FNA cytology calls other than PDAC orbenign are miscalls. Since this analysis was completed, one of thosepatients has developed symptoms of progressive pancreatic disease(weight loss, mass infiltration into duodenum, etc.). This patient iscurrently being followed to obtain a more definitive diagnosis. If thispatient develops pancreatic cancer, the accuracy of FNA benign cytologywill decrease to 59.09%. There were 36 specimens in the category ofinconclusive cytology, which could not be categorized as PDAC or benignusing cytology alone. Following the routine preoperative managementalgorithm these patients will undergo a repeat FNA procedure with a hopeof obtaining diagnostic specimen.

Subsequently, we evaluated the performance of the MP test on the samepatient population. In the group of 126 PDAC specimens by FNA cytology,the MP test accurately identified 111/126 specimens (88.09%) as PDAC.Within the group of 22 benign specimens by FNA cytology, MP accuratelyidentified 13 out of 14 true benign specimens as well as 3 out of 8 truePDAC specimens which were missed by cytology. The one patient which iscurrently progressing toward pancreatic cancer was identified as PDAC byMP. In addition, in the group of 36 patients with inconclusive cytology(cytology was neither PDAC nor benign) the test was able to provide aconfirmatory diagnosis of PDAC for 20/30 (66.67%) patients with PDAC,and confirm benign in 6/6 patients (100%). Overall, the test accuratelyclassified 134 PDAC specimens and 19 benign specimens, for an overallaccuracy of 83.15% (FIG. 20).

The performance of the MP test in conjunction with FNA cytology on the184 FNA specimens was evaluated using following assumptions: an FNAspecimen was determined to be PDAC if either FNA cytology or MP reportedit as PDAC. Otherwise, the specimen was classified as benign. Thisapproach allowed us to combine the best features of these two diagnostictools: superior accuracy of malignant FNA cytology and the highspecificity and PPV of MP in the benign and inconclusive FNA cytologyspecimens. As a result, a total of 149 out of 164 PDAC specimens and 19out of 20 benign specimens were accurately classified, resulting in anincrease of the diagnostic accuracy to 91.3% from 76.09% for FNAcytology alone (See FIG. 19). An important and clinically relevantapplication of MP is classification of samples that are presumablybenign based on cytological diagnosis of benign, as well asclassification of specimens with inconclusive cytology calls. In thiscontext (excluding all specimens called PDAC by cytology), the overallaccuracy of MP is 72.41% as compared to 24.14% for FNA cytology alone.In addition to resolving 3 out of 8 false negative calls in the benignby cytology group, MP enabled accurate identification of 20/30 PDACspecimens and all benign specimens in the group of specimensinconclusive by cytology (See FIG. 19B).

(In Tables 2-4 the sequential numerical identifiers in the leftmostcolumn refers to the same specimen across tables)

TABLE 2 miR-217 miR-375 miR-130b miR-135b miR-148a miR-155 miR-210miR-24 miR-196a miR-223 miR-96 miR-21 1 38.90 27.03 30.61 28.82 28.8434.20 26.85 25.64 35.74 28.54 41.25 24.39 2 38.39 31.73 29.92 29.7828.99 32.72 26.39 24.88 35.76 24.42 34.89 23.02 3 38.85 33.90 33.3037.90 32.54 37.78 30.91 29.70 39.16 28.21 38.81 28.34 4 36.56 28.4232.16 36.28 30.86 30.94 27.61 25.98 37.92 30.30 40.49 24.17 5 27.6121.04 22.89 24.03 22.33 26.92 21.98 20.26 31.68 19.88 27.13 18.27 630.67 24.62 30.28 35.43 27.34 35.62 30.32 27.07 40.32 29.17 35.68 26.327 39.44 23.44 24.94 24.29 24.56 27.78 23.88 20.99 31.49 20.20 28.5017.79 8 27.99 21.26 26.13 28.62 24.11 28.62 24.62 22.22 33.72 21.9531.61 20.74 9 27.53 20.57 24.40 32.00 22.55 29.31 23.80 22.36 34.6821.63 30.49 20.88 10 34.40 22.32 25.94 25.80 25.77 27.35 22.23 21.1534.50 22.78 32.23 19.74 11 28.25 21.42 24.55 25.08 22.75 27.73 23.8520.65 33.78 19.93 30.09 18.57 12 39.39 21.58 24.85 23.67 24.77 25.6220.94 19.32 29.45 19.48 29.85 17.12 13 NA 31.68 36.66 33.21 35.00 38.1631.04 30.55 39.57 33.34 40.71 28.37 14 36.46 22.00 25.99 25.48 23.7527.96 24.72 21.00 33.03 22.10 30.68 18.48 15 37.68 27.53 28.17 28.0527.56 32.55 27.52 25.39 35.52 24.32 32.10 21.39 16 24.08 18.98 24.0227.60 20.70 29.60 25.87 21.22 36.02 22.41 30.20 19.60 17 37.88 26.2725.59 28.26 26.25 29.22 23.76 21.67 35.75 21.47 30.97 20.06 18 28.2921.54 27.50 29.04 25.06 30.60 25.61 23.13 36.77 22.64 35.23 21.25 1930.23 21.54 25.68 23.72 25.31 28.68 24.20 20.42 31.82 21.37 32.13 18.9120 28.52 21.04 24.94 31.51 24.33 28.81 23.86 21.13 35.54 19.35 33.4821.27 21 34.88 28.10 32.68 29.40 30.75 34.20 28.31 26.50 37.56 27.0636.93 NA 22 38.72 24.67 29.66 28.03 28.28 31.03 27.54 23.51 36.51 24.9437.54 NA 23 36.10 27.34 31.71 28.62 29.65 33.48 28.37 25.78 37.05 27.4037.36 NA 24 34.90 23.30 24.83 23.55 24.39 25.70 21.78 19.60 29.07 18.6732.21 NA 25 36.36 22.72 26.16 23.79 25.23 28.55 22.63 21.28 35.11 23.6233.43 NA 26 23.65 18.05 22.05 26.19 19.52 25.23 22.30 19.17 31.72 18.1829.08 NA 27 31.12 20.92 23.84 23.59 23.88 25.25 21.63 19.29 33.38 18.7831.82 NA 28 32.14 19.30 24.11 20.59 23.03 25.15 21.77 18.07 28.68 18.8230.37 NA 29 33.99 24.04 22.40 25.29 22.62 23.74 20.26 18.41 30.09 16.5028.18 NA 30 27.15 21.03 23.09 23.61 22.94 23.96 21.48 19.10 27.34 19.6832.84 NA 31 25.99 19.72 25.17 26.33 21.99 29.11 25.48 21.23 36.67 22.4631.31 NA 32 35.61 21.68 27.04 24.61 25.87 28.21 24.70 23.27 35.40 20.9432.73 NA 33 25.16 20.24 24.49 20.68 21.87 25.07 21.06 18.55 29.64 18.6530.16 NA 34 34.08 21.26 22.26 23.64 23.18 25.36 20.81 19.08 30.98 15.6429.09 NA 35 35.96 21.34 25.91 26.12 25.06 27.80 21.66 20.80 35.39 20.8931.67 19.89 36 36.89 21.38 25.90 24.81 25.92 28.10 22.48 20.55 31.0121.62 32.22 20.10 37 36.61 23.47 24.26 25.24 23.29 27.01 22.51 20.1429.72 17.02 29.10 17.05 38 33.07 24.51 29.87 29.42 29.05 33.40 26.8124.13 33.87 24.55 33.59 24.00 39 38.60 23.18 28.96 27.68 26.84 31.8426.10 24.24 37.70 24.06 35.13 22.99 40 30.78 21.95 25.45 30.32 25.6228.71 23.06 21.29 36.35 21.27 32.75 20.39 41 26.17 19.93 23.45 26.9222.26 28.19 22.19 20.25 34.14 18.57 29.84 18.91 42 27.16 20.01 25.3223.81 22.97 27.08 21.89 20.32 30.98 21.18 32.95 18.36 43 40.00 22.7626.12 30.41 25.83 31.78 23.63 23.09 35.68 22.38 33.36 21.06 44 33.4318.97 27.38 22.57 23.69 27.30 22.05 19.72 30.89 22.94 33.17 17.66 4524.90 19.21 24.46 25.93 21.28 25.90 22.13 20.12 31.16 21.75 32.92 18.7346 32.10 26.92 30.73 30.46 28.39 34.86 29.68 27.53 37.79 28.72 38.4823.62 47 33.17 23.37 23.56 24.32 23.57 27.74 21.27 19.67 30.59 18.3330.63 18.13 48 28.10 19.16 23.46 22.91 23.11 25.68 21.24 17.90 31.2119.10 28.86 17.01 49 38.03 23.57 27.71 26.31 26.24 29.75 22.47 21.0532.64 24.77 33.62 19.05 50 35.62 24.54 26.50 30.02 26.81 30.58 24.2323.04 36.61 23.71 33.39 21.06 51 NA 31.31 31.35 41.66 31.56 37.38 29.1528.30 43.17 29.35 37.16 26.09 52 34.98 19.73 25.74 25.40 25.20 27.1223.08 20.42 29.43 22.69 32.50 19.48 53 29.50 22.07 28.39 27.83 24.5131.60 26.72 23.61 36.65 27.24 37.42 21.31 54 34.76 20.72 24.96 22.7821.77 29.08 24.03 20.37 29.52 22.63 30.24 17.76 55 31.48 19.96 24.1824.79 24.15 26.26 20.93 19.03 30.13 19.24 29.56 17.77 56 38.79 24.9227.35 26.65 25.89 31.09 25.08 23.03 32.90 25.64 32.10 21.52 57 NA 29.9436.08 36.40 33.53 38.69 31.78 30.03 42.39 33.87 44.09 27.58 58 36.2621.67 25.77 23.80 24.07 26.69 20.89 18.98 30.38 20.44 30.88 16.93 5930.13 23.54 29.64 34.89 26.14 34.72 29.25 26.08 42.29 29.31 37.86 24.8260 31.80 21.11 26.63 25.08 24.69 29.50 24.23 20.49 35.45 23.67 33.8517.93 61 36.31 22.43 26.18 24.54 25.22 26.93 22.04 19.32 34.61 17.8232.95 18.08 62 31.72 21.06 24.37 28.47 24.44 27.81 23.06 20.89 36.2122.61 32.65 19.53 63 NA 25.36 31.42 30.20 29.73 35.89 27.33 26.07 42.3829.41 39.19 23.90 64 41.69 29.52 30.84 26.06 29.63 34.95 26.24 23.2637.30 27.09 35.30 21.74 65 37.42 23.26 30.96 34.13 28.38 30.57 29.0325.39 41.07 28.25 35.63 23.80 66 NA 28.80 31.79 31.75 30.40 34.80 29.9626.85 38.50 29.64 35.15 23.45 67 29.15 19.61 22.69 23.86 22.55 24.9821.16 18.71 29.88 16.40 28.45 NA 68 31.34 20.20 25.09 21.97 24.01 25.6721.01 18.62 29.62 18.33 29.97 NA 69 35.03 22.08 25.69 27.25 23.19 28.4924.58 21.44 34.06 20.31 30.96 NA 70 35.34 22.69 23.32 23.02 23.51 25.1220.41 18.64 28.62 16.81 28.02 NA 71 27.22 19.93 26.18 25.97 23.21 26.7323.91 21.34 32.77 22.33 33.16 NA 72 33.87 23.61 25.45 24.73 24.08 28.0823.45 21.21 30.97 19.30 28.74 NA 73 39.38 23.81 29.21 25.73 27.81 28.1324.97 22.93 33.97 22.97 35.29 NA 74 36.50 20.52 25.72 23.23 25.32 26.0421.07 19.63 29.69 21.11 33.40 NA 75 33.56 21.79 26.69 23.11 24.92 27.7323.77 21.13 31.98 22.42 32.13 18.37 76 38.27 24.20 26.74 28.93 25.1129.87 24.72 22.20 34.92 22.13 31.79 20.63 77 35.69 22.74 25.61 23.3824.18 27.12 21.88 19.54 33.65 18.74 30.44 18.08 78 35.84 21.34 28.0823.06 25.98 28.31 24.09 21.42 30.66 22.26 33.29 19.33 79 35.83 22.4824.60 21.94 24.24 28.26 20.99 19.52 30.84 20.74 32.48 17.16 80 33.3521.92 23.62 22.71 23.28 26.02 20.18 19.05 31.33 18.87 30.77 15.98 8136.79 24.50 28.00 27.72 26.28 30.23 25.00 21.86 35.55 22.97 37.61 20.4782 NA 25.29 33.40 32.70 32.39 32.22 33.73 25.95 39.98 NA 43.22 26.40 8337.47 21.82 27.77 25.01 26.76 30.19 22.58 21.38 35.70 24.60 36.76 19.3484 40.05 22.02 27.19 27.12 25.30 29.65 25.46 21.96 38.45 23.49 37.7021.13 85 35.89 21.11 26.56 24.18 25.65 27.49 23.50 20.31 34.48 21.9834.90 19.54 86 40.42 25.15 26.78 23.89 27.56 31.18 25.90 22.82 35.8921.69 37.99 21.73 87 37.19 24.64 25.38 24.33 26.52 29.83 23.15 21.3240.48 22.05 35.91 21.05 88 NA 32.39 27.64 28.70 31.52 27.18 26.63 21.6938.92 NA 37.37 21.25 89 38.84 25.08 27.80 27.45 28.17 28.40 26.53 22.2235.43 NA 35.33 21.01 90 42.21 23.36 30.19 27.05 29.73 30.01 26.16 21.9742.19 NA 39.02 22.91 91 39.38 23.37 28.15 28.92 28.53 32.01 25.13 24.2035.78 NA 36.33 22.33 92 28.78 24.52 30.12 33.34 29.42 32.19 30.11 23.4539.31 NA 39.39 23.61 93 32.25 22.62 28.64 30.58 27.32 32.53 27.94 24.7540.72 NA 37.31 24.74 94 29.62 24.12 30.50 29.19 29.43 30.46 30.49 22.7139.39 NA 41.51 23.18 95 39.58 24.72 31.76 29.38 32.37 29.76 30.53 22.8736.50 NA 41.72 23.10 96 37.52 26.77 28.86 25.78 30.28 27.09 29.20 20.6933.30 NA 36.88 20.74 97 27.52 21.33 23.45 23.29 22.75 25.59 20.88 19.0529.42 15.73 29.44 NA 98 35.63 23.75 27.64 25.72 25.98 27.84 22.53 21.0832.19 21.33 34.16 NA 99 34.57 21.26 25.85 22.94 24.00 26.59 23.09 19.7332.35 16.87 32.27 NA 100 36.25 22.91 25.64 21.91 24.79 24.29 22.45 18.2732.52 18.30 31.12 NA 101 36.55 20.56 26.08 24.83 25.00 27.31 22.12 19.9534.10 21.11 32.22 NA 102 27.84 21.15 24.67 22.88 24.01 26.08 21.02 19.6932.75 18.21 30.16 NA 103 37.26 24.30 26.93 27.92 27.41 27.29 24.41 22.2932.41 19.20 32.71 NA 104 36.12 22.59 26.12 24.05 26.63 27.51 23.47 21.0030.71 18.89 32.83 NA 105 36.27 22.99 25.40 24.89 24.84 25.72 22.04 19.1131.40 20.28 31.90 NA 106 38.04 22.67 25.90 29.78 25.76 27.63 23.66 21.3932.86 18.34 33.13 NA 107 26.90 20.97 24.16 21.85 22.78 25.63 21.06 18.9629.33 16.53 32.10 NA 108 27.47 19.97 24.76 22.44 22.47 26.39 21.26 18.5628.90 17.69 31.93 NA 109 34.92 22.06 27.32 26.98 26.78 27.38 23.86 21.4031.03 18.86 33.42 NA 110 37.04 22.09 27.63 25.26 25.47 30.08 25.73 22.2834.86 20.88 33.17 19.01 111 36.04 21.99 26.25 23.90 25.44 29.84 23.6921.08 32.23 21.77 33.22 17.65 112 35.65 22.70 26.27 24.18 25.47 28.9023.61 20.85 31.05 21.71 33.43 17.64 113 25.91 19.61 24.28 30.16 22.1529.90 24.90 21.62 35.79 19.51 32.71 21.14 114 28.12 22.54 26.94 24.5024.13 29.08 24.18 21.95 32.52 20.64 34.88 18.92 115 30.47 23.01 25.6830.96 25.30 30.15 24.05 22.65 36.31 20.64 33.93 21.43 116 38.23 25.2627.28 31.68 26.63 31.18 26.09 24.11 36.25 21.96 33.78 20.54 117 29.8222.76 22.56 26.89 23.15 27.73 21.70 20.34 34.58 18.02 30.51 18.58 11831.61 20.61 23.31 24.13 23.67 29.28 22.39 20.13 33.45 18.52 30.68 18.27119 40.60 24.27 26.37 27.15 25.68 29.88 23.43 21.82 35.58 20.28 33.5120.56 120 31.56 21.20 24.47 22.96 25.56 28.39 21.59 19.93 33.50 19.8631.86 19.04 121 35.97 24.55 26.31 23.05 25.95 28.20 21.69 19.77 33.3022.44 33.73 17.62 122 34.28 16.90 23.46 24.16 24.27 27.96 20.70 19.3827.77 21.67 30.98 19.62 123 28.54 21.23 25.32 24.53 25.11 27.89 23.0419.88 35.55 19.29 33.13 18.70 124 34.98 23.06 26.11 24.89 26.80 27.4921.22 20.13 31.50 20.28 35.11 20.04 125 32.38 20.49 25.38 26.79 26.8828.56 23.23 20.71 31.20 20.93 34.02 20.37 126 32.45 22.19 27.31 29.1625.69 31.50 25.71 23.23 36.77 23.24 34.33 22.77 127 26.99 20.82 24.9124.43 23.66 27.67 21.79 19.24 31.28 19.71 32.04 18.58 128 27.28 21.0224.89 25.38 23.81 27.51 22.74 19.50 32.09 19.70 32.15 19.72 129 37.6623.76 28.06 26.55 28.48 30.21 23.46 21.94 33.54 22.32 35.20 21.24 13030.17 21.56 27.47 26.75 25.36 28.94 24.03 21.80 32.10 22.75 35.94 20.24131 35.77 21.59 25.96 24.52 23.59 28.11 21.92 19.03 30.96 19.34 32.9718.75 132 NA 27.37 31.47 33.19 32.54 36.36 27.21 27.27 42.37 28.59 37.6125.94 133 NA 27.05 31.58 31.00 29.36 36.43 29.88 27.10 41.81 28.41 36.9023.53 134 43.61 26.80 28.12 28.56 29.01 31.34 25.54 23.53 36.00 22.4234.46 21.75 135 32.24 23.95 29.95 30.52 28.07 30.43 27.82 24.39 39.1526.45 37.57 NA 136 32.54 24.48 30.47 27.59 28.18 32.43 26.47 24.33 35.5528.55 36.58 NA 137 34.47 21.25 26.76 26.84 26.63 28.13 23.96 21.14 33.2921.00 33.61 NA 138 35.92 28.78 31.09 28.14 30.38 33.67 27.59 24.77 37.2025.70 37.43 NA 139 29.41 18.81 24.77 25.79 24.75 28.10 23.45 20.67 34.3820.15 31.72 NA 140 43.18 28.18 32.81 33.62 30.65 35.99 31.60 27.53 42.0525.28 40.58 NA 141 31.59 18.86 23.94 24.09 24.83 27.17 22.08 19.47 33.6619.15 32.31 NA 142 35.40 28.98 34.26 34.00 32.38 38.13 31.96 29.06 42.7830.03 42.69 NA 143 29.19 23.32 29.60 33.41 26.28 34.44 28.34 25.86 40.3927.39 39.99 25.48 144 35.98 19.62 23.95 21.97 23.67 26.38 20.28 19.4534.28 18.45 29.95 NA 145 31.02 21.68 24.89 21.78 24.23 25.36 22.07 18.6028.47 19.43 31.41 16.68 146 42.14 24.12 29.33 25.17 25.58 31.18 26.0023.76 34.30 24.23 34.71 19.94 147 36.67 23.74 23.69 27.36 23.76 22.7921.79 17.80 32.17 16.24 32.32 18.11 148 28.82 21.68 27.90 29.61 25.6030.95 26.76 24.34 38.59 24.38 37.80 23.23 149 31.13 19.82 25.31 25.4424.29 27.39 23.23 21.09 32.34 19.73 31.01 18.78 150 30.77 23.64 25.7526.08 22.91 26.49 24.14 19.44 35.23 15.35 33.50 18.20 151 33.76 21.7625.06 22.02 23.35 26.91 20.07 18.75 32.43 19.39 32.76 16.43 152 39.7524.94 30.40 27.59 30.18 30.91 24.78 24.33 34.99 26.39 38.42 22.46 15335.18 22.39 25.30 22.14 24.71 25.16 20.81 18.97 29.68 17.03 32.03 NA 15435.35 22.32 28.48 27.26 27.32 30.01 23.83 22.26 36.95 23.57 37.96 21.17155 32.12 20.58 25.08 23.94 23.11 26.40 21.60 19.86 35.34 20.81 32.7417.49 156 38.28 24.79 31.57 29.34 30.64 37.14 26.32 25.14 38.98 30.6241.50 23.29 157 33.21 24.93 32.22 35.70 28.67 37.67 29.71 27.15 44.0429.96 42.93 25.72 158 38.07 23.90 28.01 25.66 27.91 28.29 22.07 20.5734.09 21.58 36.72 19.78 159 37.48 26.04 28.05 25.87 26.53 30.61 24.0121.31 33.42 19.41 36.37 20.56 160 30.51 21.30 25.60 23.64 24.24 27.2521.48 19.60 30.37 21.53 33.40 17.43 161 28.28 20.39 25.36 24.94 23.4626.89 22.38 19.81 29.19 18.72 32.26 17.78 162 30.74 21.95 23.84 21.6524.39 25.81 23.31 18.69 28.05 21.65 30.77 NA 163 32.41 18.45 23.05 25.2621.73 26.76 21.32 19.16 30.59 18.32 29.21 17.63 164 34.48 20.97 25.1221.85 23.15 25.63 22.82 18.53 29.58 19.73 30.32 16.99 165 33.54 26.7330.30 32.34 29.28 35.29 28.44 27.14 40.78 28.44 35.82 25.83 166 27.5920.71 24.72 28.66 22.89 27.28 22.85 20.64 35.09 19.81 29.75 18.97 16735.78 26.03 27.68 26.23 26.72 32.42 25.97 22.64 38.16 23.62 33.17 20.80168 31.84 21.04 23.87 22.48 22.42 25.45 21.02 18.10 27.96 18.66 29.5215.62 169 35.47 22.63 25.98 22.76 23.98 27.60 21.08 18.20 30.08 22.5130.01 16.41 170 26.98 21.47 25.99 24.57 23.94 29.27 22.41 19.92 33.1820.85 33.48 18.05 171 34.20 20.50 24.31 24.65 22.65 29.13 22.86 20.5233.06 19.77 30.25 17.40 172 24.20 19.33 25.43 27.59 23.00 28.31 22.9820.23 35.88 18.12 35.35 19.97 173 26.26 21.56 25.87 25.12 23.03 29.7723.95 20.28 35.48 22.48 33.11 17.49 174 33.20 22.56 27.46 24.50 25.3529.13 22.98 20.19 33.35 21.55 34.57 18.48 175 35.39 21.23 26.91 22.7725.16 27.37 22.13 19.99 29.50 21.48 34.28 18.89 176 37.21 24.73 27.0925.35 27.28 30.60 22.97 21.43 31.97 19.90 37.27 20.04 177 25.34 19.3824.32 23.89 21.19 28.46 23.98 19.36 32.85 18.77 33.17 18.13 178 30.8126.71 30.11 31.71 27.21 30.47 30.68 27.24 41.79 26.96 38.28 24.17 17928.33 20.54 24.51 21.55 23.26 25.80 20.82 18.35 29.11 18.20 29.19 NA 18040.05 26.58 28.71 33.86 27.46 32.28 25.62 24.23 37.59 22.74 34.10 22.54181 36.88 22.13 25.82 22.12 25.46 26.47 20.43 19.12 26.03 19.35 32.5417.32 182 38.10 28.22 31.98 31.00 29.67 35.26 31.10 27.45 40.76 29.2239.44 23.80 183 35.99 19.85 24.79 21.19 24.29 26.18 21.59 19.15 29.3518.50 30.45 NA 184 29.94 21.20 25.00 21.10 24.96 26.99 20.51 19.05 27.9019.76 30.66 NA

TABLE 3 Diff(miR- Diff(miR- Diff(miR- Diff(miR- Diff(miR- Diff(miR-Diff(miR- 135b, miR-24) 130b, miR-135b) 135b, miR-148a) 148a, miR-196a)375, miR-135b) 135b, miR-96) 130b, miR-24) 1 3.18 1.79 −0.02 −6.90 −1.79−12.43 4.96 2 4.91 0.14 0.79 −6.78 1.95 −5.11 5.04 3 8.20 −4.61 5.37−6.62 −4.00 −0.91 3.59 4 10.29 −4.12 5.42 −7.06 −7.86 −4.21 6.18 5 3.77−1.14 1.71 −9.36 −2.99 −3.09 2.63 6 8.36 −5.14 8.09 −12.98 −10.81 −0.263.22 7 3.30 0.65 −0.28 −6.92 −0.85 −4.21 3.95 8 6.40 −2.49 4.51 −9.61−7.36 −2.99 3.91 9 9.64 −7.59 9.44 −12.12 −11.42 1.50 2.05 10 4.65 0.140.03 −8.73 −3.49 −6.43 4.79 11 4.43 −0.53 2.33 −11.02 −3.66 −5.01 3.8912 4.35 1.18 −1.10 −4.68 −2.08 −6.18 5.53 13 2.66 3.45 −1.78 −4.57 −1.53−7.50 6.11 14 4.48 0.51 1.73 −9.28 −3.48 −5.20 4.99 15 2.66 0.12 0.49−7.95 −0.52 −4.05 2.78 16 6.37 −3.58 6.89 −15.32 −8.61 −2.60 2.79 176.59 −2.68 2.01 −9.50 −1.99 −2.71 3.92 18 5.91 −1.54 3.98 −11.71 −7.50−6.19 4.37 19 3.31 1.96 −1.59 −6.51 −2.18 −8.41 5.27 20 10.38 −6.57 7.18−11.21 −10.47 −1.97 3.81 21 2.90 3.27 −1.34 −6.82 −1.31 −7.52 6.18 224.52 1.63 −0.25 −8.23 −3.36 −9.51 6.15 23 2.84 3.09 −1.03 −7.41 −1.28−8.75 5.93 24 3.96 1.27 −0.84 −4.68 −0.25 −8.66 5.23 25 2.52 2.36 −1.44−9.88 −1.07 −9.64 4.88 26 7.02 −4.14 6.66 −12.20 −8.14 −2.89 2.88 274.30 0.25 −0.29 −9.50 −2.68 −8.22 4.55 28 2.52 3.52 −2.44 −5.65 −1.29−9.78 6.04 29 6.88 −2.89 2.67 −7.47 −1.25 −2.89 3.99 30 4.51 −0.52 0.67−4.40 −2.58 −9.23 3.99 31 5.10 −1.16 4.34 −14.68 −6.61 −4.98 3.94 321.34 2.43 −1.26 −9.53 −2.93 −8.12 3.77 33 2.14 3.80 −1.19 −7.77 −0.44−9.48 5.94 34 4.56 −1.38 0.46 −7.80 −2.38 −5.45 3.18 35 5.32 −0.21 1.06−10.33 −4.79 −5.54 5.11 36 4.26 1.09 −1.11 −5.09 −3.44 −7.40 5.35 375.10 −0.98 1.95 −6.43 −1.77 −3.86 4.12 38 5.29 0.44 0.38 −4.82 −4.91−4.17 5.73 39 3.45 1.27 0.85 −10.87 −4.50 −7.44 4.72 40 9.03 −4.88 4.70−10.73 −8.38 −2.43 4.16 41 6.66 −3.47 4.66 −11.88 −6.98 −2.92 3.19 423.49 1.51 0.84 −8.01 −3.80 −9.14 5.00 43 7.32 −4.28 4.58 −9.85 −7.65−2.95 3.04 44 2.85 4.80 −1.12 −7.20 −3.60 −10.60 7.66 45 5.81 −1.47 4.65−9.87 −6.72 −6.99 4.34 46 2.93 0.27 2.06 −9.40 −3.54 −8.03 3.20 47 4.65−0.76 0.75 −7.02 −0.95 −6.31 3.88 48 5.01 0.55 −0.20 −8.10 −3.75 −5.955.56 49 5.26 1.40 0.07 −6.39 −2.74 −7.31 6.66 50 6.97 −3.51 3.21 −9.80−5.48 −3.37 3.46 51 13.36 −10.32 10.10 −11.62 −10.35 4.50 3.05 52 4.990.33 0.20 −4.23 −5.67 −7.09 5.32 53 4.22 0.55 3.33 −12.15 −5.76 −9.584.78 54 2.41 2.18 1.02 −7.75 −2.07 −7.46 4.59 55 5.77 −0.62 0.65 −5.99−4.83 −4.77 5.15 56 3.62 0.71 0.76 −7.01 −1.72 −5.46 4.33 57 6.37 −0.322.87 −8.86 −6.46 −7.69 6.05 58 4.82 1.96 −0.27 −6.31 −2.13 −7.07 6.79 598.81 −5.25 8.75 −16.15 −11.35 −2.97 3.56 60 4.59 1.55 0.39 −10.76 −3.98−8.77 6.14 61 5.22 1.64 −0.68 −9.39 −2.10 −8.41 6.86 62 7.59 −4.10 4.03−11.77 −7.41 −4.18 3.49 63 4.12 1.22 0.47 −12.65 −4.84 −8.99 5.34 642.80 4.78 −3.57 −7.67 3.46 −9.24 7.58 65 8.74 −3.17 5.76 −12.69 −10.87−1.49 5.57 66 4.90 0.04 1.35 −8.10 −2.94 −3.40 4.94 67 5.15 −1.17 1.31−7.33 −4.25 −4.59 3.98 68 3.34 3.13 −2.04 −5.61 −1.77 −8.01 6.47 69 5.81−1.56 4.06 −10.86 −5.17 −3.71 4.25 70 4.38 0.30 −0.49 −5.11 −0.33 −5.004.68 71 4.63 0.21 2.76 −9.55 −6.04 −7.19 4.84 72 3.52 0.71 0.65 −6.89−1.13 −4.01 4.24 73 2.80 3.48 −2.08 −6.17 −1.91 −9.56 6.28 74 3.59 2.49−2.09 −4.37 −2.71 −10.17 6.08 75 1.99 3.58 −1.81 −7.06 −1.32 −9.01 5.5676 6.74 −2.19 3.82 −9.80 −4.73 −2.86 4.54 77 3.84 2.23 −0.79 −9.47 −0.64−7.05 6.07 78 1.64 5.02 −2.92 −4.67 −1.72 −10.23 6.66 79 2.42 2.66 −2.29−6.61 0.54 −10.53 5.08 80 3.67 0.91 −0.56 −8.05 −0.79 −8.06 4.58 81 5.850.29 1.44 −9.27 −3.22 −9.90 6.14 82 6.75 0.70 0.32 −7.59 −7.41 −10.517.45 83 3.63 2.76 −1.75 −8.94 −3.19 −11.75 6.39 84 5.16 0.07 1.82 −13.15−5.10 −10.58 5.23 85 3.87 2.38 −1.47 −8.83 −3.07 −10.72 6.25 86 1.072.89 −3.67 −8.33 1.26 −14.10 3.96 87 3.00 1.05 −2.19 −13.96 0.31 −11.584.05 88 7.01 −1.06 −2.82 −7.40 3.69 −8.67 5.95 89 5.23 0.35 −0.72 −7.26−2.37 −7.88 5.58 90 5.09 3.14 −2.68 −12.46 −3.69 −11.97 8.23 91 4.72−0.77 0.39 −7.25 −5.55 −7.40 3.95 92 9.90 −3.22 3.92 −9.89 −8.82 −6.046.67 93 5.83 −1.94 3.26 −13.40 −7.97 −6.73 3.89 94 6.48 1.31 −0.24 −9.96−5.07 −12.32 7.79 95 6.51 2.38 −2.99 −4.13 −4.66 −12.33 8.89 96 5.093.08 −4.50 −3.01 0.99 −11.10 8.17 97 4.23 0.17 0.54 −6.67 −1.96 −6.164.40 98 4.63 1.92 −0.26 −6.21 −1.96 −8.44 6.56 99 3.21 2.91 −1.06 −8.35−1.68 −9.33 6.13 100 3.63 3.73 −2.88 −7.73 1.01 −9.21 7.36 101 4.87 1.25−0.18 −9.10 −4.27 −7.40 6.13 102 3.19 1.78 −1.13 −8.74 −1.74 −7.27 4.98103 5.63 −0.99 0.51 −5.00 −3.62 −4.79 4.64 104 3.05 2.07 −2.58 −4.08−1.45 −8.78 5.12 105 5.78 0.50 0.05 −6.56 −1.90 −7.01 6.28 106 8.40−3.89 4.03 −7.11 −7.11 −3.34 4.51 107 2.89 2.31 −0.93 −6.55 −0.88 −10.255.20 108 3.88 2.32 −0.03 −6.43 −2.47 −9.49 6.20 109 5.58 0.34 0.20 −4.25−4.92 −6.44 5.92 110 2.98 2.37 −0.21 −9.39 −3.17 −7.91 5.35 111 2.822.35 −1.54 −6.79 −1.91 −9.32 5.17 112 3.33 2.09 −1.29 −5.59 −1.48 −9.265.42 113 8.54 −5.88 8.01 −13.63 −10.55 −2.55 2.67 114 2.55 2.44 0.37−8.39 −1.97 −10.38 4.99 115 8.31 −5.28 5.67 −11.01 −7.95 −2.96 3.03 1167.56 −4.40 5.05 −9.62 −6.42 −2.10 3.16 117 6.55 −4.33 3.74 −11.44 −4.13−3.62 2.22 118 4.00 −0.82 0.46 −9.78 −3.52 −6.55 3.18 119 5.33 −0.781.47 −9.90 −2.88 −6.36 4.55 120 3.03 1.51 −2.60 −7.94 −1.76 −8.89 4.54121 3.28 3.26 −2.90 −7.35 1.50 −10.67 6.54 122 4.78 −0.70 −0.11 −3.50−7.26 −6.83 4.08 123 4.65 0.79 −0.59 −10.43 −3.30 −8.60 5.44 124 4.761.22 −1.91 −4.69 −1.83 −10.22 5.98 125 6.07 −1.41 −0.09 −4.33 −6.30−7.24 4.67 126 5.92 −1.85 3.47 −11.08 −6.97 −5.17 4.08 127 5.19 0.480.77 −7.62 −3.61 −7.61 5.66 128 5.88 −0.49 1.57 −8.28 −4.36 −6.77 5.39129 4.60 1.51 −1.94 −5.06 −2.79 −8.66 6.12 130 4.96 0.72 1.39 −6.74−5.19 −9.18 5.67 131 5.49 1.44 0.92 −7.36 −2.93 −8.45 6.93 132 5.92−1.72 0.65 −9.83 −5.83 −4.42 4.20 133 3.90 0.58 1.63 −12.45 −3.95 −5.914.48 134 5.03 −0.44 −0.45 −7.00 −1.75 −5.90 4.59 135 6.13 −0.58 2.45−11.08 −6.57 −7.04 5.55 136 3.26 2.88 −0.58 −7.37 −3.11 −8.99 6.14 1375.70 −0.08 0.21 −6.66 −5.59 −6.77 5.62 138 3.36 2.95 −2.24 −6.83 0.64−9.30 6.32 139 5.12 −1.02 1.04 −9.63 −6.98 −5.93 4.11 140 6.09 −0.812.96 −11.40 −5.44 −6.97 5.28 141 4.62 −0.15 −0.74 −8.83 −5.23 −8.22 4.47142 4.95 0.25 1.63 −10.41 −5.03 −8.68 5.20 143 7.55 −3.81 7.14 −14.12−10.09 −6.57 3.74 144 2.52 1.97 −1.70 −10.60 −2.36 −7.98 4.50 145 3.173.11 −2.45 −4.24 −0.10 −9.64 6.28 146 1.40 4.16 −0.42 −8.72 −1.05 −9.545.56 147 9.56 −3.67 3.60 −8.42 −3.62 −4.96 5.89 148 5.27 −1.71 4.01−12.99 −7.93 −8.19 3.56 149 4.35 −0.12 1.14 −8.05 −5.62 −5.57 4.23 1506.63 −0.32 3.16 −12.32 −2.44 −7.43 6.31 151 3.27 3.03 −1.33 −9.08 −0.26−10.74 6.31 152 3.26 2.81 −2.59 −4.81 −2.65 −10.83 6.07 153 3.17 3.16−2.57 −4.96 0.25 −9.89 6.33 154 5.00 1.22 −0.06 −9.63 −4.94 −10.70 6.22155 4.09 1.14 0.84 −12.23 −3.36 −8.79 5.23 156 4.19 2.23 −1.30 −8.34−4.55 −12.16 6.43 157 8.54 −3.48 7.02 −15.36 −10.77 −7.24 5.06 158 5.092.35 −2.25 −6.18 −1.75 −11.07 7.44 159 4.57 2.18 −0.66 −6.89 0.17 −10.496.75 160 4.05 1.95 −0.59 −6.13 −2.35 −9.75 6.00 161 5.14 0.42 1.49 −5.74−4.56 −7.31 5.55 162 2.97 2.19 −2.74 −3.66 0.30 −9.11 5.16 163 6.10−2.21 3.53 −8.86 −6.81 −3.95 3.89 164 3.32 3.27 −1.30 −6.43 −0.89 −8.466.59 165 5.19 −2.04 3.06 −11.50 −5.61 −3.49 3.15 166 8.02 −3.94 5.78−12.21 −7.95 −1.09 4.08 167 3.58 1.45 −0.49 −11.45 −0.19 −6.94 5.04 1684.38 1.40 0.06 −5.53 −1.44 −7.04 5.77 169 4.56 3.22 −1.21 −6.11 −0.13−7.25 7.78 170 4.65 1.42 0.63 −9.24 −3.10 −8.91 6.07 171 4.13 −0.34 1.99−10.40 −4.15 −5.60 3.79 172 7.36 −2.15 4.59 −12.88 −8.26 −7.76 5.20 1734.84 0.75 2.10 −12.45 −3.56 −7.99 5.59 174 4.30 2.96 −0.85 −8.01 −1.94−10.07 7.27 175 2.79 4.13 −2.38 −4.34 −1.54 −11.51 6.92 176 3.93 1.73−1.92 −4.70 −0.62 −11.92 5.66 177 4.53 0.43 2.70 −11.67 −4.51 −9.28 4.96178 4.47 −1.60 4.50 −14.58 −5.00 −6.58 2.87 179 3.20 2.96 −1.71 −5.85−1.01 −7.64 6.16 180 9.63 −5.15 6.40 −10.12 −7.28 −0.24 4.48 181 3.003.70 −3.34 −0.57 0.01 −10.42 6.69 182 3.56 0.98 1.33 −11.09 −2.78 −8.444.53 183 2.05 3.60 −3.10 −5.06 −1.34 −9.26 5.65 184 2.05 3.90 −3.86−2.94 0.10 −9.57 5.95

TABLE 4 miRInformPancreasScore miRInformPancreasCall Cytology TruthCalledCorrectly 1 1.00 PDAC NonDiagnostic PDAC TRUE 2 0.98 PDAC AtypicalPDAC TRUE 3 0.00 Benign Atypical PDAC FALSE 4 0.00 Benign Benign BenignTRUE 5 1.00 PDAC Benign Benign FALSE 6 0.00 Benign Benign PDAC FALSE 71.00 PDAC Atypical PDAC TRUE 8 0.05 Benign PDAC PDAC FALSE 9 0.00 BenignNonDiagnostic Benign TRUE 10 0.99 PDAC PDAC PDAC TRUE 11 0.93 PDACNonDiagnostic PDAC TRUE 12 1.00 PDAC Benign PDAC TRUE 13 1.00 PDACSuspicious PDAC TRUE 14 0.92 PDAC PDAC PDAC TRUE 15 1.00 PDAC BenignPDAC TRUE 16 0.00 Benign Benign PDAC FALSE 17 0.12 Benign NonDiagnosticPDAC FALSE 18 0.05 Benign Benign Benign TRUE 19 1.00 PDAC SuspiciousPDAC TRUE 20 0.00 Benign Benign PDAC FALSE 21 1.00 PDAC PDAC PDAC TRUE22 0.99 PDAC PDAC PDAC TRUE 23 1.00 PDAC PDAC PDAC TRUE 24 1.00 PDACPDAC PDAC TRUE 25 1.00 PDAC Suspicious PDAC TRUE 26 0.00 Benign PDACPDAC FALSE 27 1.00 PDAC PDAC PDAC TRUE 28 1.00 PDAC Suspicious PDAC TRUE29 0.19 Benign PDAC PDAC FALSE 30 1.00 PDAC PDAC PDAC TRUE 31 0.02Benign PDAC PDAC FALSE 32 1.00 PDAC PDAC PDAC TRUE 33 1.00 PDAC PDACPDAC TRUE 34 1.00 PDAC PDAC PDAC TRUE 35 0.51 PDAC PDAC PDAC TRUE 361.00 PDAC PDAC PDAC TRUE 37 0.99 PDAC PDAC PDAC TRUE 38 0.98 PDAC PDACPDAC TRUE 39 1.00 PDAC PDAC PDAC TRUE 40 0.00 Benign Suspicious PDACFALSE 41 0.01 Benign Benign Benign TRUE 42 1.00 PDAC Suspicious PDACTRUE 43 0.03 Benign Suspicious PDAC FALSE 44 1.00 PDAC PDAC PDAC TRUE 450.24 Benign PDAC PDAC FALSE 46 1.00 PDAC PDAC PDAC TRUE 47 1.00 PDACPDAC PDAC TRUE 48 0.95 PDAC PDAC PDAC TRUE 49 0.88 PDAC PDAC PDAC TRUE50 0.07 Benign PDAC PDAC FALSE 51 0.00 Benign PDAC PDAC FALSE 52 1.00PDAC PDAC PDAC TRUE 53 0.83 PDAC PDAC PDAC TRUE 54 1.00 PDAC SuspiciousPDAC TRUE 55 0.95 PDAC PDAC PDAC TRUE 56 1.00 PDAC PDAC PDAC TRUE 570.06 Benign PDAC PDAC FALSE 58 0.95 PDAC PDAC PDAC TRUE 59 0.00 BenignBenign PDAC FALSE 60 0.75 PDAC PDAC PDAC TRUE 61 0.50 PDAC PDAC PDACTRUE 62 0.00 Benign Suspicious PDAC FALSE 63 0.99 PDAC PDAC PDAC TRUE 641.00 PDAC PDAC PDAC TRUE 65 0.00 Benign NonDiagnostic Benign TRUE 660.89 PDAC PDAC PDAC TRUE 67 0.99 PDAC PDAC PDAC TRUE 68 1.00 PDAC PDACPDAC TRUE 69 0.05 Benign Benign Benign TRUE 70 1.00 PDAC PDAC PDAC TRUE71 0.91 PDAC PDAC PDAC TRUE 72 1.00 PDAC PDAC PDAC TRUE 73 1.00 PDACPDAC PDAC TRUE 74 1.00 PDAC PDAC PDAC TRUE 75 1.00 PDAC PDAC PDAC TRUE76 0.01 Benign PDAC PDAC FALSE 77 0.98 PDAC PDAC PDAC TRUE 78 1.00 PDACPDAC PDAC TRUE 79 1.00 PDAC PDAC PDAC TRUE 80 1.00 PDAC PDAC PDAC TRUE81 0.30 Benign PDAC PDAC FALSE 82 0.02 Benign Benign Benign TRUE 83 1.00PDAC PDAC PDAC TRUE 84 0.30 Benign Atypical Benign TRUE 85 1.00 PDACPDAC PDAC TRUE 86 1.00 PDAC PDAC PDAC TRUE 87 1.00 PDAC PDAC PDAC TRUE88 0.67 PDAC PDAC PDAC TRUE 89 0.99 PDAC PDAC PDAC TRUE 90 0.51 PDACPDAC PDAC TRUE 91 1.00 PDAC PDAC PDAC TRUE 92 0.00 Benign Benign BenignTRUE 93 0.14 Benign Benign Benign TRUE 94 0.01 Benign PDAC PDAC FALSE 950.68 PDAC PDAC PDAC TRUE 96 1.00 PDAC PDAC PDAC TRUE 97 1.00 PDAC PDACPDAC TRUE 98 0.99 PDAC PDAC PDAC TRUE 99 1.00 PDAC Atypical PDAC TRUE100 1.00 PDAC PDAC PDAC TRUE 101 0.87 PDAC Atypical PDAC TRUE 102 1.00PDAC PDAC PDAC TRUE 103 0.99 PDAC PDAC PDAC TRUE 104 1.00 PDAC PDAC PDACTRUE 105 0.78 PDAC PDAC PDAC TRUE 106 0.00 Benign Suspicious PDAC FALSE107 1.00 PDAC PDAC PDAC TRUE 108 1.00 PDAC PDAC PDAC TRUE 109 0.99 PDACPDAC PDAC TRUE 110 1.00 PDAC PDAC PDAC TRUE 111 1.00 PDAC SuspiciousPDAC TRUE 112 1.00 PDAC PDAC PDAC TRUE 113 0.00 Benign Atypical PDACFALSE 114 1.00 PDAC NonDiagnostic PDAC TRUE 115 0.00 Benign AtypicalPDAC FALSE 116 0.01 Benign PDAC PDAC FALSE 117 0.26 Benign Benign PDACFALSE 118 1.00 PDAC Benign PDAC TRUE 119 0.80 PDAC PDAC PDAC TRUE 1201.00 PDAC Atypical PDAC TRUE 121 1.00 PDAC PDAC PDAC TRUE 122 1.00 PDACPDAC PDAC TRUE 123 0.96 PDAC PDAC PDAC TRUE 124 1.00 PDAC PDAC PDAC TRUE125 1.00 PDAC PDAC PDAC TRUE 126 0.14 Benign PDAC PDAC FALSE 127 0.94PDAC PDAC PDAC TRUE 128 0.53 PDAC PDAC PDAC TRUE 129 1.00 PDAC PDAC PDACTRUE 130 0.99 PDAC PDAC PDAC TRUE 131 0.49 Benign PDAC PDAC FALSE 1320.95 PDAC PDAC PDAC TRUE 133 0.98 PDAC Suspicious PDAC TRUE 134 1.00PDAC PDAC PDAC TRUE 135 0.03 Benign Atypical Benign TRUE 136 1.00 PDACPDAC PDAC TRUE 137 0.94 PDAC PDAC PDAC TRUE 138 1.00 PDAC PDAC PDAC TRUE139 0.97 PDAC PDAC PDAC TRUE 140 0.10 Benign Benign Benign TRUE 141 1.00PDAC PDAC PDAC TRUE 142 0.95 PDAC PDAC PDAC TRUE 143 0.00 BenignAtypical PDAC FALSE 144 1.00 PDAC PDAC PDAC TRUE 145 1.00 PDACSuspicious PDAC TRUE 146 1.00 PDAC PDAC PDAC TRUE 147 0.00 Benign BenignBenign TRUE 148 0.47 Benign Benign Benign TRUE 149 1.00 PDAC AtypicalPDAC TRUE 150 0.00 Benign Atypical Benign TRUE 151 1.00 PDAC PDAC PDACTRUE 152 1.00 PDAC PDAC PDAC TRUE 153 1.00 PDAC PDAC PDAC TRUE 154 0.91PDAC PDAC PDAC TRUE 155 0.91 PDAC PDAC PDAC TRUE 156 1.00 PDAC PDAC PDACTRUE 157 0.00 Benign Benign Benign TRUE 158 0.99 PDAC PDAC PDAC TRUE 1590.99 PDAC PDAC PDAC TRUE 160 1.00 PDAC PDAC PDAC TRUE 161 0.98 PDAC PDACPDAC TRUE 162 1.00 PDAC PDAC PDAC TRUE 163 0.40 Benign PDAC PDAC FALSE164 1.00 PDAC Suspicious PDAC TRUE 165 0.82 PDAC PDAC PDAC TRUE 166 0.00Benign Benign Benign TRUE 167 0.99 PDAC PDAC PDAC TRUE 168 1.00 PDACPDAC PDAC TRUE 169 0.92 PDAC PDAC PDAC TRUE 170 0.91 PDAC PDAC PDAC TRUE171 0.99 PDAC Suspicious PDAC TRUE 172 0.00 Benign Benign Benign TRUE173 0.17 Benign Suspicious PDAC FALSE 174 0.97 PDAC PDAC PDAC TRUE 1751.00 PDAC PDAC PDAC TRUE 176 1.00 PDAC PDAC PDAC TRUE 177 0.78 PDAC PDACPDAC TRUE 178 0.88 PDAC PDAC PDAC TRUE 179 1.00 PDAC PDAC PDAC TRUE 1800.00 Benign NonDiagnostic Benign TRUE 181 1.00 PDAC PDAC PDAC TRUE 1821.00 PDAC PDAC PDAC TRUE 183 1.00 PDAC PDAC PDAC TRUE 184 1.00 PDAC PDACPDAC TRUE

REFERENCES

The following references, to the extent that they provide exemplaryprocedural or other details supplementary to those set forth herein, arespecifically incorporated herein by reference.

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1. A method for determining whether a patient has pancreatic ductaladenocarcinoma (PDAC) comprising: a) measuring from a pancreatic samplefrom the patient the level of expression of at least two of thefollowing diff pair miRNAs: miR-135b, miR148a, miR-130b, miR-196a,miR-24, miR-375, miR-96, miR-155, miR-21, miR-24, miR-201, miR-217, andmiR-223, wherein at least one of the miRNAs is a biomarker miRNA and oneis a comparative miRNA; b) determining at least one biomarker diff pairvalue based on the level of expression of the biomarker miRNA comparedto the level of expression of the comparative miRNA; and, c) evaluatingwhether the pancreatic sample comprises PDAC cells based on thebiomarker diff pair value(s).
 2. (canceled)
 3. The method of claim 1,wherein the level of at least five of the diff pair miRNAs are measured.4.-6. (canceled)
 7. The method of claim 1, wherein the pancreatic sampleis a fine needle aspirate, a macrodissected sample, or a formalin-fixedparaffin embedded (PPFE) sample. 8.-12. (canceled)
 13. The method ofclaim 1, wherein the level of at least miR-135b is measured. 14.-25.(canceled)
 26. The method of claim 1, wherein a diff pair value isdetermined for at least one of the following diff pairs:miR-135b/mir-24; miR-130b/miR-135b; miR-135b/miR-148a;miR-148a/miR-196a; miR-375/miR-135b; miR-135b/miR-96; miR-155/miR-21 ormiR-130b/miR-24.
 27. The method of claim 26, wherein a diff pair valueis determined for miR-135b/mir-24. 28.-36. (canceled)
 37. The method ofclaim 1, further comprising determining a risk score of PDAC for thepatient. 38.-39. (canceled)
 40. The method of claim 1, furthercomprising wherein the sample has undergone a cytology analysis prior tomeasuring the level of expression of any miRNA.
 41. The method of claim1, further comprising treating the patient for PDAC if the patient isdetermined to be more likely to have PDAC than not to have PDAC. 42.-44.(canceled)
 45. A method for characterizing pancreatic cells in a patientcomprising determining the level of miR-135b expression in a biologicalsample comprising pancreas cells.
 46. (canceled)
 47. The method of claim45, further comprising comparing the determined level of miR-135bexpression in the biological sample to a normalized level of miR-135b innoncancerous pancreas cells.
 48. The method of claim 45 furthercomprising comparing the determined level of miR-135b expression in thebiological sample to a determined level of a second miRNA in thebiological sample.
 49. The method of claim 48, further comprisingdetermining the level of expression of the second miRNA in thebiological sample.
 50. The method of claim 48, wherein the level of thesecond miRNA is miR-148a, miR-130b, miR-196a, miR-24, miR-375, ormiR-96.
 51. The method of claim 45, further comprising determining thelevel of miR-148a, miR-130b, miR-196a, miR-24, miR-375, or miR-96.52.-63. (canceled)
 64. The method of claim 45, further comprisingdetermining the level of miR-24 expression in the biological sample. 65.The method of claim 64, further comprising comparing the level ofmiR-135b expression to the level of miR-24 expression in the biologicalsample.
 66. (canceled)
 67. The method of claim 45, further comprisingdetermining the level of expression of a second miRNA in the biologicalsample.
 68. The method of claim 67, wherein the level of expression ofthe second miRNA is miR-148a, miR-130b, miR-196a, miR-24, miR-375, ormiR-96. 69.-71. (canceled)
 72. The method of claim 67, furthercomprising determining the level of a third mRNA that is miR-148a,miR-130b, miR-196a, miR-24, miR-375, or miR-96. 73.-191. (canceled) 192.A method for identifying pancreatic cancer cells in a patientcomprising: a) obtaining a pancreatic sample from the patient, b) fromthe pancreatic sample, measuring expression levels of at least miR-135band at least one of the following miRNAs: miR-24, miR-96, miR-130b,miR-148a, miR-196a, or miR-375; c) calculating a differential valuebetween the level of miR-135b expression and a level of expression of areference miRNA in the sample to provide at least one differential pairanalysis factor; and d) evaluating one or more differential pairanalysis factors using a scoring algorithm to generate a risk score forthe presence of pancreatic cancer cells in the pancreatic sample,wherein the patient is identified as having or as not having pancreaticcancer cells based on the score.
 193. The method of claim 192, whereinthe expression level of miR-24 is measured to provide the expressionlevel of a reference miRNA in the sample for calculating a differentialvalue between the level of miR-135b expression and the level ofexpression of a reference miRNA in the sample to provide at least onedifferential pair analysis factor. 194.-199. (canceled)
 200. The methodof claim 192, further comprising: (a) calculating a differential valuebetween the level of miR-135b expression and the level of miR-24expression to provide at least one differential pair analysis factor;(b) calculating a differential value between the level of miR-135bexpression and the level of miR-130b expression to provide at least onedifferential pair analysis factor; (c) calculating a differential valuebetween the level of miR-135b expression and the level of miR-148aexpression to provide at least one differential pair analysis factor;and (d) calculating a differential value between the level of miR-148aexpression and the level of miR-196a expression to provide at least onedifferential pair analysis factor.
 201. The method of claim 200, furthercomprising: (a) calculating a differential value between the level ofmiR-135b expression and the level of miR-375 expression to provide atleast one differential pair analysis factor; and (b) calculating adifferential value between the level of miR-135b expression and thelevel of miR-96 expression to provide at least one differential pairanalysis factor. 202.-210. (canceled)